Academic literature on the topic 'Natural conversation'
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Journal articles on the topic "Natural conversation"
1
Dipta, Dinar. "Conversation Analysis as a Discourse Approach to Teaching Speaking Skill." JETLe (Journal of English Language Teaching and Learning) 1, no. 1 (2019): 1. http://dx.doi.org/10.18860/jetle.v1i1.7718.
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<p>Conversation comes as two ways communication used in daily activities. The large numbers of conversations lead some researchers to analyze it. Conversation has been the primary interest to language researchers since natural, unplanned, everyday conversation is the most commonly occurring and universal language genre (Riggenbach, 1999). Conversation is a speech activity in which all members of a community routinely participate. Among other approaches to discourse analysis used in teaching speaking, conversation analysis (CA) is one of the convenient practical devices in teaching oral English in the classroom. This paper aims to explore the theoretical basis for conversational analysis and examine the techniques applying a discourse approach to teaching speaking in the classroom.</p>
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Joty, S., G. Carenini, and R. T. Ng. "Topic Segmentation and Labeling in Asynchronous Conversations." Journal of Artificial Intelligence Research 47 (July 22, 2013): 521–73. http://dx.doi.org/10.1613/jair.3940.
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Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.
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Saifudin, Akhmad. "Implikatur Percakapan dalam Studi Linguistik Pragmatik (Conversational Implicature in Pragmatic Linguistic Studies)." JALABAHASA 16, no. 1 (2020): 15. http://dx.doi.org/10.36567/jalabahasa.v16i1.423.
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Tulisan ini membahas implikatur percakapan, yakni sebuah studi dalam ilmu linguistik pragmatik yang mengkaji maksud penutur dalam percakapan. Tujuan penulisan ini adalah untuk mendeskripsikan apa itu implikatur percakapan, bagaimana mengidentifikasi dan memaknai implikatur, serta mengapa penutur menggunakan implikatur dalam tuturannya. Untuk mengkaji permasalahan digunakan teori Grice tentang prinsip kerja sama (PK), maksim percakapan (MP), dan implikatur percakapan. Data percakapan diperoleh dari observasi percakapan natural antara penulis dan mahasiswa, serta percakapan di antara mahasiswa yang terjadi di lingkungan kampus. Hasil analisis menunjukkan bahwa pada dasarnya dalam setiap percakapan digunakan implikatur. Implikatur digunakan bukan karena tidak ingin bekerja sama dalam percakapan, tetapi ada alasan yang lebih diprioritaskan dan alasan tersebut tidak dapat mematuhi semua maksim dalam MP. Implikatur percakapan digunakan untuk tujuan efisiensi verbal, pengalihan tanpa berbohong, kesopanan, dan tujuan estetika, serta ironi. This paper discusses the conversational implicature, which is a study in pragmatic linguistics that examines the intent of speakers in conversation. The purpose of this paper is to describe what the conversational implicature is, how to identify and interpret the implicature, and why do speakers want to engage in implicature. To study the problem Grice's theory of the cooperative principle, maxims of conversation, and the conversational implicature are used. Conversation data is obtained from observations of natural conversations between writers and students, as well as conversations between students that occur on campus. The results of the analysis show that basically in every conversation the implicature is used; The implicature is used not because they do not want to cooperate in conversation, but because there are prioritized reasons and those reasons cannot comply with all maxims in maxims of conversation. The conversationalimplicature is used for the purpose of verbal efficiency, misleading to lying, politeness, and aesthetic purposes, as well as irony.
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Althoff, Tim, Kevin Clark, and Jure Leskovec. "Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health." Transactions of the Association for Computational Linguistics 4 (December 2016): 463–76. http://dx.doi.org/10.1162/tacl_a_00111.
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Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.
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Nishfullayli, Sa'idatun, Lea Santiar, and Harni Kartika Ningsih. "Discourse Structure Analysis of Making Request in Japanese Conversation." JAPANEDU: Jurnal Pendidikan dan Pengajaran Bahasa Jepang 8, no. 2 (2023): 94–110. http://dx.doi.org/10.17509/japanedu.v8i2.61548.
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Making requests (irai) is a genre of spoken interaction that is taught from the basic level of learning Japanese as a foreign language. A request is one of the speech acts that may raise face-threatening potentials. Understanding the stages of request appropriate to Japanese culture is thus essential for Japanese learners to achieve successful conversation. Therefore, conversation pedagogy by using a discourse approach is essential. This study investigates a potential structure gap in Japanese making-requests conversations realized in actual settings and textbook conversational models. By employing genre theory and interpersonal discourse of “Negotiation” as a qualitative discourse analytic method from the Systemic Functional Linguistics (SFL) perspective, this paper describes the gaps and some factors that potentially influence the structure of Japanese making-request conversation. Data were obtained from conversational texts in the Japanese language corpus named Japanese Natural Conversation Corpus and Japanese textbooks for elementary and middle adult learners. Regarding the structure, the results show no difference between conversations in textbooks and authentic ones at the stage level, but both differ at the phase level. There is no introduction to the problem, additional explanation, and confirmation phases in textbook conversational models. In addition, the absence of the phases, the differences in pre-condition content between textbook and authentic conversations, also the length of the reasoning phase, are assumed to be influenced by relational status between participants (tenor) as well as the imposition degree of the requested object.
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McGRATH, LARRY S. "Quit Playing Language Games with My Heart; or, Conversational AI and Knowledge Sharing." Ethnographic Praxis in Industry Conference Proceedings 2024, no. 1 (2024): 255–76. https://doi.org/10.1111/epic.12205.
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This paper shares guidance for designing conversational AI based on findings from linguistic and social analysis of core shifts introduced by large language models. When Duolingo and Babbel added AI features to their language learning apps, the games that users play on the apps was transformed, giving us a window into the structure of AI‐driven user interactions in general. Dynamic, turn‐taking dialogues resembling natural conversation now appear alongside multiple‐choice and drag‐and‐drop vocabulary games. The transformation is not just technological: It also lays bare the shifting language games that we have come to play more broadly in our interactions with LLMs such as Chat‐GPT, Claude, and Gemini. People engage with them in back‐and‐forth conversations in which different kinds of speech acts are at play. By contrast, traditional conversational agents based on decision‐tree learning rely on discrete, finite, and close‐ended commands. The paper argues that the future of AI conversation is one in which rule‐based interactions co‐exist with intention‐based interactions powered by generative AI. Design elements on Duolingo, in particular, draw on diverse styles of communication and offer guidance to ensure that conversational AI platforms in general remain open to new forms of dialogue that enrich both artificial and natural conversation alike.
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Hassan, Mahmudul, Shahriar Shakil, Nazmun Nessa Moon, et al. "Sentiment analysis on Bangla conversation using machine learning approach." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5562. http://dx.doi.org/10.11591/ijece.v12i5.pp5562-5572.
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<span>Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.</span>
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Mahmudul, Hassan, Shakil Shahriar, Nessa Moon Nazmun, et al. "Sentiment analysis on Bangla conversation using machine learning approach." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): Detection approach. https://doi.org/10.11591/ijece.v12i5.pp5562-5572.
Full textAbstract:
Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
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Wimbassa, Muhamad Dwirizqy, Taswiyah Marsyah Noor, Salma Yasara, Vannesha Vannesha, Tubagus Muhammad Arsyah, and Abdiansah Abdiansah. "Emotional Text Detection dengan Long Short Term Memory (LSTM)." Format : Jurnal Ilmiah Teknik Informatika 12, no. 2 (2023): 158. http://dx.doi.org/10.22441/format.2023.v12.i2.009.
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Emotional Text Detection is a technique in natural language processing that aims to identify the emotions contained in conversations or text messages. The LSTM (Long Short-Term Memory) method is one of the techniques used in natural language processing to model and predict sequential data. In this study, we propose the use of the LSTM method for emotion detection in conversation. The dataset used is a conversational dataset that contains positive, negative, and neutral emotions. We process datasets using data pre-processing techniques such as tokenization, data cleansing and one-hot encoding. Then, we train the LSTM model on the processed dataset and obtain evaluation results using accuracy metrics. The experimental results show that the LSTM model can be used to detect emotions in conversation with a good degree of accuracy. In addition, we also conducted an analysis on the prediction results of the model and showed that the LSTM model can correctly identify emotions. In conclusion, the LSTM method can be used to detect emotions in conversation with a good degree of accuracy. This method can be used to improve user experience in chat applications and increase the effectiveness of human and machine interactions.
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Ansari, Tarique. "Conversational AI Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1169–72. http://dx.doi.org/10.22214/ijraset.2022.47554.
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Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.
Dissertations / Theses on the topic "Natural conversation"
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Dalacorte, Maria Cristina Faria. "Natural conversation and efltextbook dialogues : a constrastive study." reponame:Repositório Institucional da UFSC, 1991. https://repositorio.ufsc.br/xmlui/handle/123456789/157696.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão<br>Made available in DSpace on 2016-01-08T17:10:36Z (GMT). No. of bitstreams: 1
84295.pdf: 1903258 bytes, checksum: 45c467c4c73cf96b76630645a7fc3cda (MD5)
Previous issue date: 1991<br>Examina características estruturais, estratégicas e estilísticas de interações comerciais e conversações por telefone em inglês e português comparadas com diálogos escritos de livros para o ensino de língua inglesa. Estes livros afirmam ensinar inglês através de diálogos reais. Esta análise verifica se as conversações apresentadas nestes livros textos demonstram características semelhantes, às de conversações naturais. Através de uma análise contrastiva detalhada dos dois tipos de diálogos, este estudo prova que as conversações dos livros de diálogos de inglês não são comunicativas mas pseudo- interativas, já que apresentam características da estrutura interna do discurso de sala de aula.
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au, os goh@murdoch edu, and Ong Sing Goh. "A framework and evaluation of conversation agents." Murdoch University, 2008. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20081020.134601.
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This project details the development of a novel and practical framework for the development of conversation agents (CAs), or conversation robots. CAs, are software programs which can be used to provide a natural interface between human and computers. In this study, conversation refers to real-time dialogue exchange between human and machine which may range from web chatting to on-the-go conversation through mobile devices. In essence, the project proposes a smart and effective communication technology where an autonomous agent is able to carry out simulated human conversation via multiple channels. The CA developed in this project is termed Artificial Intelligence Natural-language Identity (AINI) and AINI is used to illustrate the implementation and testing carried out in this project. Up to now, most CAs have been developed with a short term objective to serve as tools to convince users that they are talking with real humans as in the case of the Turing Test. The traditional designs have mainly relied on ad-hoc approach and hand-crafted domain knowledge. Such approaches make it difficult for a fully integrated system to be developed and modified for other domain applications and tasks. The proposed framework in this thesis addresses such limitations. Overcoming the weaknesses of previous systems have been the key challenges in this study. The research in this study has provided a better understanding of the system requirements and the development of a systematic approach for the construction of intelligent CAs based on agent architecture using a modular N-tiered approach. This study demonstrates an effective implementation and exploration of the new paradigm of Computer Mediated Conversation (CMC) through CAs. The most significant aspect of the proposed framework is its ability to re-use and encapsulate expertise such as domain knowledge, natural language query and human-computer interface through plug-in components. As a result, the developer does not need to change the framework implementation for different applications. This proposed system provides interoperability among heterogeneous systems and it has the flexibility to be adapted for other languages, interface designs and domain applications. A modular design of knowledge representation facilitates the creation of the CA knowledge bases. This enables easier integration of open-domain and domain-specific knowledge with the ability to provide answers for broader queries. In order to build the knowledge base for the CAs, this study has also proposed a mechanism to gather information from commonsense collaborative knowledge and online web documents. The proposed Automated Knowledge Extraction Agent (AKEA) has been used for the extraction of unstructured knowledge from the Web. On the other hand, it is also realised that it is important to establish the trustworthiness of the sources of information. This thesis introduces a Web Knowledge Trust Model (WKTM) to establish the trustworthiness of the sources.
In order to assess the proposed framework, relevant tools and application modules have been developed and an evaluation of their effectiveness has been carried out to validate the performance and accuracy of the system. Both laboratory and public experiments with online users in real-time have been carried out. The results have shown that the proposed system is effective. In addition, it has been demonstrated that the CA could be implemented on the Web, mobile services and Instant Messaging (IM). In the real-time human-machine conversation experiment, it was shown that AINI is able to carry out conversations with human users by providing spontaneous interaction in an unconstrained setting. The study observed that AINI and humans share common properties in linguistic features and paralinguistic cues. These human-computer interactions have been analysed and contributed to the understanding of how the users interact with CAs. Such knowledge is also useful for the development of conversation systems utilising the commonalities found in these interactions. While AINI is found having difficulties in responding to some forms of paralinguistic cues, this could lead to research directions for further work to improve the CA performance in the future.
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Niekrasz, John Joseph. "Toward summarization of communicative activities in spoken conversation." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6449.
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This thesis is an inquiry into the nature and structure of face-to-face conversation, with a special focus on group meetings in the workplace. I argue that conversations are composed of episodes, each of which corresponds to an identifiable communicative activity such as giving instructions or telling a story. These activities are important because they are part of participants’ commonsense understanding of what happens in a conversation. They appear in natural summaries of conversations such as meeting minutes, and participants talk about them within the conversation itself. Episodic communicative activities therefore represent an essential component of practical, commonsense descriptions of conversations. The thesis objective is to provide a deeper understanding of how such activities may be recognized and differentiated from one another, and to develop a computational method for doing so automatically. The experiments are thus intended as initial steps toward future applications that will require analysis of such activities, such as an automatic minute-taker for workplace meetings, a browser for broadcast news archives, or an automatic decision mapper for planning interactions. My main theoretical contribution is to propose a novel analytical framework called participant relational analysis. The proposal argues that communicative activities are principally indicated through participant-relational features, i.e., expressions of relationships between participants and the dialogue. Participant-relational features, such as subjective language, verbal reference to the participants, and the distribution of speech activity amongst the participants, are therefore argued to be a principal means for analyzing the nature and structure of communicative activities. I then apply the proposed framework to two computational problems: automatic discourse segmentation and automatic discourse segment labeling. The first set of experiments test whether participant-relational features can serve as a basis for automatically segmenting conversations into discourse segments, e.g., activity episodes. Results show that they are effective across different levels of segmentation and different corpora, and indeed sometimes more effective than the commonly-used method of using semantic links between content words, i.e., lexical cohesion. They also show that feature performance is highly dependent on segment type, suggesting that human-annotated “topic segments” are in fact a multi-dimensional, heterogeneous collection of topic and activity-oriented units. Analysis of commonly used evaluation measures, performed in conjunction with the segmentation experiments, reveals that they fail to penalize substantially defective results due to inherent biases in the measures. I therefore preface the experiments with a comprehensive analysis of these biases and a proposal for a novel evaluation measure. A reevaluation of state-of-the-art segmentation algorithms using the novel measure produces substantially different results from previous studies. This raises serious questions about the effectiveness of some state-of-the-art algorithms and helps to identify the most appropriate ones to employ in the subsequent experiments. I also preface the experiments with an investigation of participant reference, an important type of participant-relational feature. I propose an annotation scheme with novel distinctions for vagueness, discourse function, and addressing-based referent inclusion, each of which are assessed for inter-coder reliability. The produced dataset includes annotations of 11,000 occasions of person-referring. The second set of experiments concern the use of participant-relational features to automatically identify labels for discourse segments. In contrast to assigning semantic topic labels, such as topical headlines, the proposed algorithm automatically labels segments according to activity type, e.g., presentation, discussion, and evaluation. The method is unsupervised and does not learn from annotated ground truth labels. Rather, it induces the labels through correlations between discourse segment boundaries and the occurrence of bracketing meta-discourse, i.e., occasions when the participants talk explicitly about what has just occurred or what is about to occur. Results show that bracketing meta-discourse is an effective basis for identifying some labels automatically, but that its use is limited if global correlations to segment features are not employed. This thesis addresses important pre-requisites to the automatic summarization of conversation. What I provide is a novel activity-oriented perspective on how summarization should be approached, and a novel participant-relational approach to conversational analysis. The experimental results show that analysis of participant-relational features is a.
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Goh, Ong Sing. "A framework and evaluation of conversation agents." Thesis, Goh, Ong Sing (2008) A framework and evaluation of conversation agents. PhD thesis, Murdoch University, 2008. https://researchrepository.murdoch.edu.au/id/eprint/752/.
Full textAbstract:
This project details the development of a novel and practical framework for the development of conversation agents (CAs), or conversation robots. CAs, are software programs which can be used to provide a natural interface between human and computers. In this study, ‘conversation’ refers to real-time dialogue exchange between human and machine which may range from web chatting to “on-the-go” conversation through mobile devices. In essence, the project proposes a “smart and effective” communication technology where an autonomous agent is able to carry out simulated human conversation via multiple channels. The CA developed in this project is termed “Artificial Intelligence Natural-language Identity” (AINI) and AINI is used to illustrate the implementation and testing carried out in this project. Up to now, most CAs have been developed with a short term objective to serve as tools to convince users that they are talking with real humans as in the case of the Turing Test. The traditional designs have mainly relied on ad-hoc approach and hand-crafted domain knowledge. Such approaches make it difficult for a fully integrated system to be developed and modified for other domain applications and tasks. The proposed framework in this thesis addresses such limitations. Overcoming the weaknesses of previous systems have been the key challenges in this study. The research in this study has provided a better understanding of the system requirements and the development of a systematic approach for the construction of intelligent CAs based on agent architecture using a modular N-tiered approach. This study demonstrates an effective implementation and exploration of the new paradigm of Computer Mediated Conversation (CMC) through CAs. The most significant aspect of the proposed framework is its ability to re-use and encapsulate expertise such as domain knowledge, natural language query and human-computer interface through plug-in components. As a result, the developer does not need to change the framework implementation for different applications. This proposed system provides interoperability among heterogeneous systems and it has the flexibility to be adapted for other languages, interface designs and domain applications. A modular design of knowledge representation facilitates the creation of the CA knowledge bases. This enables easier integration of open-domain and domain-specific knowledge with the ability to provide answers for broader queries. In order to build the knowledge base for the CAs, this study has also proposed a mechanism to gather information from commonsense collaborative knowledge and online web documents. The proposed Automated Knowledge Extraction Agent (AKEA) has been used for the extraction of unstructured knowledge from the Web. On the other hand, it is also realised that it is important to establish the trustworthiness of the sources of information. This thesis introduces a Web Knowledge Trust Model (WKTM) to establish the trustworthiness of the sources.
In order to assess the proposed framework, relevant tools and application modules have been developed and an evaluation of their effectiveness has been carried out to validate the performance and accuracy of the system. Both laboratory and public experiments with online users in real-time have been carried out. The results have shown that the proposed system is effective. In addition, it has been demonstrated that the CA could be implemented on the Web, mobile services and Instant Messaging (IM). In the real-time human-machine conversation experiment, it was shown that AINI is able to carry out conversations with human users by providing spontaneous interaction in an unconstrained setting. The study observed that AINI and humans share common properties in linguistic features and paralinguistic cues. These human-computer interactions have been analysed and contributed to the understanding of how the users interact with CAs. Such knowledge is also useful for the development of conversation systems utilising the commonalities found in these interactions. While AINI is found having difficulties in responding to some forms of paralinguistic cues, this could lead to research directions for further work to improve the CA performance in the future.
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Goh, Ong Sing. "A framework and evaluation of conversation agents." Goh, Ong Sing (2008) A framework and evaluation of conversation agents. PhD thesis, Murdoch University, 2008. http://researchrepository.murdoch.edu.au/752/.
Full textAbstract:
This project details the development of a novel and practical framework for the development of conversation agents (CAs), or conversation robots. CAs, are software programs which can be used to provide a natural interface between human and computers. In this study, ‘conversation’ refers to real-time dialogue exchange between human and machine which may range from web chatting to “on-the-go” conversation through mobile devices. In essence, the project proposes a “smart and effective” communication technology where an autonomous agent is able to carry out simulated human conversation via multiple channels. The CA developed in this project is termed “Artificial Intelligence Natural-language Identity” (AINI) and AINI is used to illustrate the implementation and testing carried out in this project. Up to now, most CAs have been developed with a short term objective to serve as tools to convince users that they are talking with real humans as in the case of the Turing Test. The traditional designs have mainly relied on ad-hoc approach and hand-crafted domain knowledge. Such approaches make it difficult for a fully integrated system to be developed and modified for other domain applications and tasks. The proposed framework in this thesis addresses such limitations. Overcoming the weaknesses of previous systems have been the key challenges in this study. The research in this study has provided a better understanding of the system requirements and the development of a systematic approach for the construction of intelligent CAs based on agent architecture using a modular N-tiered approach. This study demonstrates an effective implementation and exploration of the new paradigm of Computer Mediated Conversation (CMC) through CAs. The most significant aspect of the proposed framework is its ability to re-use and encapsulate expertise such as domain knowledge, natural language query and human-computer interface through plug-in components. As a result, the developer does not need to change the framework implementation for different applications. This proposed system provides interoperability among heterogeneous systems and it has the flexibility to be adapted for other languages, interface designs and domain applications. A modular design of knowledge representation facilitates the creation of the CA knowledge bases. This enables easier integration of open-domain and domain-specific knowledge with the ability to provide answers for broader queries. In order to build the knowledge base for the CAs, this study has also proposed a mechanism to gather information from commonsense collaborative knowledge and online web documents. The proposed Automated Knowledge Extraction Agent (AKEA) has been used for the extraction of unstructured knowledge from the Web. On the other hand, it is also realised that it is important to establish the trustworthiness of the sources of information. This thesis introduces a Web Knowledge Trust Model (WKTM) to establish the trustworthiness of the sources.
In order to assess the proposed framework, relevant tools and application modules have been developed and an evaluation of their effectiveness has been carried out to validate the performance and accuracy of the system. Both laboratory and public experiments with online users in real-time have been carried out. The results have shown that the proposed system is effective. In addition, it has been demonstrated that the CA could be implemented on the Web, mobile services and Instant Messaging (IM). In the real-time human-machine conversation experiment, it was shown that AINI is able to carry out conversations with human users by providing spontaneous interaction in an unconstrained setting. The study observed that AINI and humans share common properties in linguistic features and paralinguistic cues. These human-computer interactions have been analysed and contributed to the understanding of how the users interact with CAs. Such knowledge is also useful for the development of conversation systems utilising the commonalities found in these interactions. While AINI is found having difficulties in responding to some forms of paralinguistic cues, this could lead to research directions for further work to improve the CA performance in the future.
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Comuni, Federica. "A natural language processing solution to probable Alzheimer’s disease detection in conversation transcripts." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19889.
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This study proposes an accuracy comparison of two of the best performing machine learning algorithms in natural language processing, the Bayesian Network and the Long Short-Term Memory (LSTM) Recurrent Neural Network, in detecting Alzheimer’s disease symptoms in conversation transcripts. Because of the current global rise of life expectancy, the number of seniors affected by Alzheimer’s disease worldwide is increasing each year. Early detection is important to ensure that affected seniors take measures to relieve symptoms when possible or prepare plans before further cognitive decline occurs. Literature shows that natural language processing can be a valid tool for early diagnosis of the disease. This study found that mild dementia and possible Alzheimer’s can be detected in conversation transcripts with promising results, and that the LSTM is particularly accurate in said detection, reaching an accuracy of 86.5% on the chosen dataset. The Bayesian Network classified with an accuracy of 72.1%. The study confirms the effectiveness of a natural language processing approach to detecting Alzheimer’s disease.
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Fuscone, Simone. "A data intensive approach for characterizing speech interpersonal dynamics in natural conversations." Electronic Thesis or Diss., Aix-Marseille, 2020. http://www.theses.fr/2020AIXM0444.
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Lors d’une conversation, les participants ont la tendance à accorder, consciemment ou non, leur production communicative par rapport à leur interlocuteur. Il est généralement admis que dans des circonstances normales, ce phénomène entraîne une convergence des paramètres de parole des deux participants. Il est généralement connu que dans des circonstances normales, ce phénomène génère une convergence des paramètres du discours des deux participants. Alors que ces études impliquent souvent des conditions de laboratoire contrôlées, les mécanismes qui régissent le phénomène dans les conversations naturelles sont moins connus, en raison du flux spontané des conversants et de la grande variabilité des paramètres suivis. En outre, on ne sait pas encore très bien comment les participants modifient leur style de parole (c'est à dire la dynamique) au cours de la conversation et quels sont les facteurs qui influencent ces modifications. Cette thèse présente une nouvelle méthodologie pour aborder ces aspects<br>During a conversation, participants tend to tune, consciously or not, their communicative production in regards to their interlocutor. It is generally admitted, that under standard circumstances, these phenomena result in convergence of the two participants’ speech parameters. Past literature offers a large part of studies describing the effects of convergence in interpersonal dynamics but there are still some unclear aspects. These concerns firstly the mechanisms that rule the phenomenon in natural conversations. These are hard to be studied due to the spontaneous flow of the conversants that results to be noisy and variable. In second place in this kind of conversation is still not well known how participants modify their speech style (the dynamics i.e.) in the course of the conversation. In this thesis, we aim to validate previous results in acoustic-prosodic convergence and provide novel approaches to have a partial a posteriori filter on natural conversations and to track the interpersonal dynamics. We used classical machine learning approaches (Linear mixed models, Random forest e.g.) and more recent algorithms of deep learning (LSTM architecture). These results extend the landscape of convergence effects in the not controlled dataset and offer novel approaches, concerning the method to control the variability of natural conversations and the prediction task paradigm to evaluate the interpersonal dynamics, consisting in evaluating the influence of the speaker and interlocutor on each other speech style
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Cuenca, Montesino José María. "L’application WhatsApp dans la négociation franco-espagnole : un catalyseur de la confiance interculturelle." Thesis, Paris 10, 2017. http://www.theses.fr/2017PA100128/document.
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La convergence entre les technologies de l’information et de la communication et l’informatique a pour conséquence l’atténuation des frontières spatio-temporelles. La révolution induite par la téléphonie mobile trouve son paroxysme dans le smartphone, une génération de téléphones intelligents qui intègrent des fonctions propres à l’informatique, dont les applications. Grâce à celles-ci, la communication mobiquitaire devient un fait quotidien. L’application gratuite de messagerie instantanée WhatsApp apparait en 2009, et connaît un succès fulgurant en Espagne. Son utilisation provoque l’émergence d’un nouveau genre discursif : la « conversation ouatsap ». Ce travail de recherche prend appui sur une étude de cas à partir de laquelle seront analysées les manifestations linguistiques et pragmatiques de la confiance interpersonnelle et interculturelle lors des conversations ouatsap menées dans le contexte professionnel du secteur viticole. Nous analyserons les manifestations de celles-ci sur un corpus de conversations ouatsap authentiques menées entre une entrepreneure en France et trois de ses partenaires commerciaux espagnols<br>The convergence between information and communication technologies and informatics technologies has, as a consequence, a weakening of spatial-temporal boundaries. The ensuing revolution brought about by mobile phones finds its paroxysm with smartphones, a generation of intelligent telephones which integrates functions of informatic technologies such as the applications. Thanks to them mobiquitous communication has become an everyday reality. The free application of instant messages WhatsApp first appeared in 2009, and had a dazzling effect in Spain. It’s use led to the appearance of a new discursive gender: the ouatsap conversation. A case study in which the linguistic and pragmatic characteristics of interpersonal and intercultural trust during ouatsap conversations in a professional context in the winemaking industry underlies this research. We will analyse their characteristics in a corpus of genuine ouatsap conversations that took place between a French company and three of its Spanish commercial partners
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Liu, Yulan. "Distant speech recognition of natural spontaneous multi-party conversations." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17691/.
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Distant speech recognition (DSR) has gained wide interest recently. While deep networks keep improving ASR overall, the performance gap remains between using close-talking recordings and distant recordings. Therefore the work in this thesis aims at providing some insights for further improvement of DSR performance. The investigation starts with collecting the first multi-microphone and multi-media corpus of natural spontaneous multi-party conversations in native English with the speaker location tracked, i.e. the Sheffield Wargame Corpus (SWC). The state-of-the-art recognition systems with the acoustic models trained standalone and adapted both show word error rates (WERs) above 40% on headset recordings and above 70% on distant recordings. A comparison between SWC and AMI corpus suggests a few unique properties in the real natural spontaneous conversations, e.g. the very short utterances and the emotional speech. Further experimental analysis based on simulated data and real data quantifies the impact of such influence factors on DSR performance, and illustrates the complex interaction among multiple factors which makes the treatment of each influence factor much more difficult. The reverberation factor is studied further. It is shown that the reverberation effect on speech features could be accurately modelled with a temporal convolution in the complex spectrogram domain. Based on that a polynomial reverberation score is proposed to measure the distortion level of short utterances. Compared to existing reverberation metrics like C50, it avoids a rigid early-late-reverberation partition without compromising the performance on ranking the reverberation level of recording environments and channels. Furthermore, the existing reverberation measurement is signal independent thus unable to accurately estimate the reverberation distortion level in short recordings. Inspired by the phonetic analysis on the reverberation distortion via self-masking and overlap-masking, a novel partition of reverberation distortion into the intra-phone smearing and the inter-phone smearing is proposed, so that the reverberation distortion level is first estimated on each part and then combined.
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Campbell, Robert. "Understanding and disrupting institutional settings : using networks of conversations to re-imagine future farming lives." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/603.
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Farmers in Australia and elsewhere face the challenge of remaining profitable whilst dealing with adverse structural arrangements and public expectations to better manage environmental degradation. This thesis draws on arguments that dominant paradigms in agricultural science and environmental management have often been ineffective in addressing these apparently competing demands and appear poorly suited to ‘messy’ situations characterized by uncertainty and complexity, and in which diverse stakeholders are motivated by varying goals and values. Engaging with such situations requires a philosophy and methodology that accepts a multiplicity of perspectives and which seeks to learn about and reflect upon novel ways of thinking and acting. Among the underlying ideas that have shaped this project is the importance of recognising the assumptions and commitments that researchers bring to their practice in order that traditions are not uncritically reproduced and that the products of our thinking are not reified. Regarding farming as less a set of technical practices and more as a human activity taking place within broader economic, social, cultural and ecological contexts, I sought to engage a group of farmers in southern Western Australia in a process of taking action to address an issue of common concern that would help them to live and farm well in their district. My role as both researcher and facilitator of conversations was driven by a commitment to dialogue as a process of meaning making and relationship building. Together we explored some of the broader contexts within which the narrower conceptions of economic and ecological problems are often uncritically placed. Taking concrete action together however proved beyond the scope of my research. The challenge of feeding ourselves while better caring for the land and each other will require imaginative as well as technical resources. To this end I have also sought to sketch out some of the creative possibilities contained within the health metaphor as it is applied to soil, arguing that its use as a proxy for quality or condition fails to utilize its disruptive potential.
Books on the topic "Natural conversation"
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International Workshop on Natural, Intelligent and Effective Interaction in Multimodal Dialogue Systems (2002 : Copenhagen, Denmark). Advances in natural multimodal dialogue systems. Springer, 2004.
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van, Kuppevelt Jan, Dybkjær Laila 1959-, and Bernsen Niels Ole, eds. Advances in natural multimodal dialogue systems. Springer, 2005.
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Principe, Walter H. De veritate humanae naturae: Theology in conversation with biology, medicine, and philosophy of nature. Finnish Society for Missiology and Ecumenics, 1990.
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Darroch, John. Chinese self taught: By the natural method with phonetic pronunciation (Thimm's system). Asian Educational Services, 1999.
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Os, Leonard W. Van. Afrikaans self taught: By the natural method with phonetic pronunciation (Thimm's system). Asian Educational Services, 1999.
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Attaoullah, Fuad A. Turkish self-taught: By natural method with English phonetic pronunciation, revised, and enlarged vocabularies and phrases. Asian Educational Services, 2003.
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Pertemuan Regional Pengelolaan Taman Nasional Kawasan Timur Indonesia (1999 Manado, Indonesia). Pertemuan Regional Pengelolaan Taman Nasional Kawasan Timur Indonesia: Kelembagaan pengelolaan taman nasional. [Natural Resources Management/EPIQ Program, 1999.
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O'Beirne, John J. R. Serbo-Croatian self taught: By the natural method with the English phonetic pronunciation (Thimm's system). Asian Educational Services, 1999.
Find full textBook chapters on the topic "Natural conversation"
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Fujishin, Randy. "Conversation." In Natural Bridges in Interpersonal Communication, 3rd ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003327172-6.
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Fujishin, Randy. "Conversation." In Natural Bridges in Interpersonal Communication. Routledge, 2019. http://dx.doi.org/10.4324/9780429196935-6.
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Moore, Robert J. "A Natural Conversation Framework for Conversational UX Design." In Human–Computer Interaction Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95579-7_9.
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Jefferson, Gail. "A sketch of some orderly aspects of overlap in natural conversation." In Conversation Analysis. John Benjamins Publishing Company, 2004. http://dx.doi.org/10.1075/pbns.125.05jef.
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Szczepek Reed, Beatrice. "Intonation phrases in natural conversation." In Prosody in Interaction. John Benjamins Publishing Company, 2010. http://dx.doi.org/10.1075/sidag.23.16ree.
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Zhuang, Yimeng, Xianliang Wang, Han Zhang, Jinghui Xie, and Xuan Zhu. "An Ensemble Approach to Conversation Generation." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73618-1_5.
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Hazel, Spencer, and Adam Brandt. "Enhancing the Natural Conversation Experience Through Conversation Analysis – A Design Method." In HCI International 2023 – Late Breaking Papers. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48038-6_6.
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Drescher, Martina. "The negotiation of affect in natural conversation." In Negotiation and Power in Dialogic Interaction. John Benjamins Publishing Company, 2001. http://dx.doi.org/10.1075/cilt.214.16dre.
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Wang, Yida, Pei Ke, Yinhe Zheng, et al. "A Large-Scale Chinese Short-Text Conversation Dataset." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60450-9_8.
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Bai, Guirong, Shizhu He, Kang Liu, and Jun Zhao. "Variational Attention for Commonsense Knowledge Aware Conversation Generation." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32233-5_1.
Full textConference papers on the topic "Natural conversation"
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Choi, Bonggeun, JeongJae Park, Yoonsung Kim, Jaehyun Park, and Youngjoong Ko. "RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.108.
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Ashby, Trevor, Adithya Kulkarni, Jingyuan Qi, et al. "Towards Effective Long Conversation Generation with Dynamic Topic Tracking and Recommendation." In Proceedings of the 17th International Natural Language Generation Conference. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.inlg-main.43.
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Zheng, Zhonghua, Lizi Liao, Yang Deng, Ee-Peng Lim, Minlie Huang, and Liqiang Nie. "Thoughts to Target: Enhance Planning for Target-driven Conversation." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1175.
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Cadilhac, Anaïs, Nicholas Asher, Farah Benamara, and Alex Lascarides. "Grounding Strategic Conversation: Using Negotiation Dialogues to Predict Trades in a Win-Lose Game." In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1035.
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Won, Jong-In, Jin-Won Ko, and Jin-Woo Jung. "Recognition of User's Activeness and Consistency During Conversation and Its Application to More Natural Speech Generation." In 2025 17th International Conference on Knowledge and Smart Technology (KST). IEEE, 2025. https://doi.org/10.1109/kst65016.2025.11003293.
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Zhang, Dong, Liangqing Wu, Changlong Sun, Shoushan Li, Qiaoming Zhu, and Guodong Zhou. "Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/752.
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Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detection in conversation text has one specific challenge in modeling the context-sensitive dependence. Besides, emotion detection in multi-speaker conversations endorses another specific challenge in modeling the speaker-sensitive dependence. To address above two challenges, we propose a conversational graph-based convolutional neural network. On the one hand, our approach represents each utterance and each speaker as a node. On the other hand, the context-sensitive dependence is represented by an undirected edge between two utterances nodes from the same conversation and the speaker-sensitive dependence is represented by an undirected edge between an utterance node and its speaker node. In this way, the entire conversational corpus can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph. The experimental results on a multi-modal and multi-speaker conversation corpus demonstrate the great effectiveness of the proposed approach.
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Aubrey, Andrew J., David Marshall, Paul L. Rosin, Jason Vendeventer, Douglas W. Cunningham, and Christian Wallraven. "Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations." In 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2013. http://dx.doi.org/10.1109/cvprw.2013.48.
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Watson, Jan, Adrian Curtin, Yigit Topoglu, et al. "Natural Language Processing to Assess Communication Dynamics between Cooperating Dyads during Video Gameplay." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001827.
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Latent Dirichlet Allocation (LDA) and Sentiment Analysis have become prominent tools in natural language processing applications for both research and industry. While LDA is a generative probabilistic modeling methodology that is widely used in Topic Modeling to extract underlying themes and topics from a collection of words, Sentiment Analysis is defined as identifying the hedonic tone of a corpus of text. Here, supervised Sentiment Analysis is used to classify conversations between team gaming dyads in terms of valence. Additionally, LDA is utilized to label segments of cooperative conversation between dyads as topics. Fourteen participants were paired as dyads (7 teams) and instructed to complete thirty-two 150 second gaming scenarios (trials) in the first-person shooter (FPS) video game Overwatch. While completing the scenarios, participants were instructed to communicate with their respective teammate via a voice communication headset. The conversations from each scenario were transcribed from recorded voice channels before analysis was performed. Our approach examines the relationship between perceived task difficulty and both conversation sentiment scores and topic frequency in both novice experienced skill groups. Preliminary results indicate evidence that conversation topic, sentiment and perception dynamics are consistent with an encouragement and frustration sentiment paradigm.
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Zhang, Zhishuo, Yu Sun, and Ryan Yan. "An Intelligent Mobile Application for Depression Relief using Artificial Intelligence and Natural Language Processing." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121006.
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“What is an simple yet effective method to improve the mental health of individuals?” is the question that we chose to tackle [7]. The solution that we came up with was having a deep conversation with another person. From personal experiences, having deep conversations with another person seemed to be one of the most effective ways to keep someone's mental health issues under control and maintain a more positive outlook on life. Sharing similar experiences with another person can demonstrate to people that they are not alone and there is always someone who can relate to them and lead them down the right path. In order to provide people with an easier method to have deep conversations with one another, we decided to create an application called Affinity, which was developed using Flutter [8]. In this application, users with various mental health issues will be able to talk with other users who have shared similar experiences. Users can connect to each other based on similar mental health issues, and they can engage in deep conversations with one another through a chat messaging system. We tested the results by providing twelve participants with two surveys. One survey measured a self-given score regarding the participant’s levels of stress and anxiety before using Affinity as well as after one week of using Affinity, and the other survey asks participants to tally the number of conversation partners that shared at least one mental health issue or experience with them compared to the total number of conversation partners. The results we have found are that daily usage of this application will generally reduce levels of stress and anxiety, and the majority of the individuals that the application will offer as conversation partners will be able to connect to a user through at least one additional similar shared experience or mental health issue.
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Tyen, Gladys, Andrew Caines, and Paula Buttery. "LLM chatbots as a language practice tool: a user study." In 13th Workshop on Natural Language Processing for Computer Assisted Language Learning. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp211018.
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Second language learners often experience language anxiety when speaking with others in their target language. As the generative capabilities of Large Language Models (LLMs) continue to improve, we investigate the possibility of using an LLM as a conversation practice tool. We conduct a user study with 160 English language learners, where an LLM chatbot is used to simulate real-world conversations. We present our findings on 1) how an interactive session with a chatbot might impact performance in real-world conversations; 2) whether the learning experience differs for learners of different proficiency levels; 4) how changes in difficulty affects the learner's experience; and 3) how online, synchronous conversation provided by an LLM compares with a purely receptive experience. Additionally, we propose a simple yet effective way to detect linguistic complexity on-the-fly: clicking on words to reveal dictionary definitions. We demonstrate that clicks correlate well with linguistic complexity and indicate which words learners find difficult to understand.
Reports on the topic "Natural conversation"
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Traum, David R. Towards a Computational Theory of Grounding in Natural Language Conversation. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada248777.
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Arnup, Katherine. Family Perspectives: Death and Dying in Canada. The Vanier Institute of the Family, 2018. http://dx.doi.org/10.61959/prxz1798e.
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Death is a natural part of life, but many Canadians are hesitant to have essential conversations about the end of their lives. The Vanier Institute of the Family seeks to change this with the publication of Family Perspectives: Death and Dying in Canada, a conversation catalyst intended to spark dialogue in households, workplaces and communities across the country by exploring death and dying through a family lens. Family Perspectives: Death and Dying in Canada examines the evolution of death and dying in Canada across generations, the desires and realities of families surrounding death and dying, the role of families in end-of-life care and its impact on well-being. Through current data and trend analysis, interviews with caregivers and families, and reflections on hospice volunteering from author Dr. Katherine Arnup, this study discusses death and dying within the current and emerging social, cultural and policy landscapes.
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Archibeque, Ben, Kari O'Connell, Isabel Delgado, Christopher Nytch, Nancy Merlo, and Emily Ortiz Franco. Public Engagement with Science at Luquillo-Long-Term Ecological Research Program: Results from a Case Study. Oregon State University, 2023. http://dx.doi.org/10.5399/osu/1174.
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The goals of this work are to a) understand community perspectives related to public engagement with science and the Luquillo Long Term Ecological Research (LUQ-LTER) program, b) enhance existing relationships within these communities, and c) illuminate opportunities for establishing new relationships between the LUQ-LTER program and its surrounding community. We share results from our case study that includes three methods of data collection: interviews with natural resource managers, a community conversation activity with 12 local community organizations, and a survey given at a public outreach event at El Portal de El Yunque. From interviews with natural resource managers, we learned they are looking for collaborations which directly benefit the resources they manage (forests, water, etc.), and they are also hoping for more formal communication structures between them and the LUQ-LTER. Sixty percent of the organizations at the Community Conversation shared that the most valuable outcome was learning that LUQ-LTER existed .All participants expressed interest in collaborating with the LUQ-LTER and said that LUQ-LTER’s long term ecological data could be beneficial for them to use and LUQ-LTER could be a productive context to share data they gather to heighten utility in all directions. From a survey given at the International Day of Forests Celebration, we learned that most people present at the activity came to the El Yunque National Forest to learn about the forest, spend time with family, or enjoy the forest. We also learned that during previous visits to El Yunque, they most commonly either day hiked or visited a geographical feature (waterfall or observation tower). When asked about which activities they would like to have happen in the future in El Yunque, most people indicated high levels of interest in having educational activities (like festivals or flora and fauna workshops), workshops or training on environmental issues, and night tours. The volunteer opportunities for which the most people indicated highest levels of interest were trail, camp, and related maintenance as well as fish and wildlife opportunities. Overall, everyone we talked to was interested in more collaboration and working together in the future, giving the LUQ-LTER program an excellent starting point for building stronger relationships with the community surrounding their site.
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Imel, Zac, Ming Tai-Seale, Padhraic Smyth, and David Atkins. Identifying Topics in Patient and Doctor Conversations Using Natural Language Processing Methods. Patient-Centered Outcomes Research Institute (PCORI), 2021. http://dx.doi.org/10.25302/08.2021.me.160234167.
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Mangrulkar, Amol, Archita Suryanarayanan, Elizabeth Shilpa Abraham, et al. Built environment in IIHS campus, Kengeri. Edited by Aromar Revi, Rahul Mehotra, and Sanjay Prakash. Indian Institute for Human Settlements, 2024. https://doi.org/10.24943/9788198702364.
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The IIHS Campus, Kengeri sits at the intersection of the Ramasagara Lake, Sulikere Forest and an urban settlement, making it an excellent site to examine the interconnectedness between natural systems and the built environment. The Campus aspires to become an archetype of a safe space for humans and nature alike, to build eco-friendly and climate resilient structures, and to establish a living lab that brings together people and ideas from multiple disciplines to overcome challenges of rapid urbanisation, growing population and a heating planet. The BUILT ENVIRONMENT Box Set contains books that document debates and conversations about best practices, and early stages of development of structures on the Campus. 1. Making of the experimental building 2. Making of the Riparian Amphitheatre 3. Making of the experimental kitchen
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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, et al. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.
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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of finding conversations that reference firearm injury or violence in the Crisis Text Line database. Identifying rare categories, like firearm injury or violence, can improve crisis lines' abilities to support people with firearm-related crises or provide appropriate resources. Our approach outperforms a set of iteratively refined Boolean queries and results in a recall of 0.91 on a test set generated from a process independent of our study. Our results suggest that text classification with Boolean retrieval initialization can be effective for finding rare categories of interest and improve on the precision of using Boolean retrieval alone.
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Bhatikar, Tanvi. Cities in Flux 2023: IIHS Annual Research Conference Proceedings. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/9788195847341.
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Urban ARC is the Annual Research Conference of the Indian Institute for Human Settlements (IIHS). The seventh edition of the conference was conducted virtually and in person from 12 to 14 January 2023 and focused on the theme ‘Cities in Flux’. The panels and sessions explored, built on and continued ongoing conversations around the broad spectrum of the nature of flux in and across our cities; and how to navigate these changes, our practices must be responsive, empathetic, and value-driven. A wide range of themes, sectors, disciplines and methodologies was discussed. These Conference Proceedings feature the extended abstracts submitted by the selected participants for Urban ARC 2023
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Javed, Umair, Aiza Hussain, and Hassan Aziz. Demanding Power: Contentious Politics and Electricity in Pakistan. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/ids.2021.047.
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This paper explores Pakistan’s electricity supply crisis that lasted from 2007 to 2015, and the ensuing contention that shaped public discourse and political events in the country. During this period, which witnessed electricity outages of up to 14 hours per day, 456 incidents of contention took place, with just under 20 per cent escalating into some form of violence. Electricity became the number one political issue in the country and was integral in shaping the outcomes of the 2013 General Election. Following the election, public authorities undertook extensive investment to expand capacity and ensure consistency in supply while evading questions about affordability and sustainability. On the surface, this appears to be a case of extensive protest working towards shaping state responsiveness. And it is true that the state now sees supply as a non-negotiable aspect in the social contract with citizens. However, a range of factors contributed to the chronology and the selective, generation-focused nature of this response. On the other hand, citizen inclusion and participation in decision-making, and issues of affordability and sustainability, which impact vulnerable and disempowered groups the most, remain absent from the political and policy conversation around energy. This suggests that while protests were useful in generating a short-term response, their long-term legacy in empowerment related outcomes is less visible.
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Georgalakis, James, Saira Ahmed, Vaqar Ahmed, et al. Stories of Change: Covid-19 Responses for Equity. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/core.2023.018.
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Covid-19 Responses for Equity (CORE) was a three-year, CA$25m rapid research initiative that brought together 20 research projects to understand the socioeconomic impacts of the pandemic, improve existing responses, and generate better policy options for recovery. The research, funded by the Canadian International Development Research Centre (IDRC), took place across 42 countries in Africa, Asia, Latin America and the Middle East. The Institute of Development Studies (IDS) supported CORE to maximise the learning generated across the research portfolio and deepen engagement with governments, civil society, and the scientific community. This publication celebrates the impact of that research, and highlights Stories of Change from seven of the CORE projects that successfully influenced policy, practice, and understandings of the crisis. Collectively, these individual case studies provide a narrative about the nature of research impact in emergencies and the implications for the design and delivery of future rapid response research initiatives. There are clear lessons around the importance of organisational reputation, and the value of co-designing research with decision makers whilst simultaneously taking a critical position. Every story here emphasises the need to understand political context and to explore the trade-offs between research rigour and the timeliness of evidence. Above all, they illustrate the value of flexible funding arrangements that enable local teams to respond to fast-moving crises. These stories demonstrate unequivocally the value of locally led research responses to emergencies with the right international flow of resources and support. CORE’s research teams were well-placed to bring together communities, civil society organisations, and governments to create a space for vulnerable and marginalised groups to discuss their lived experiences of the pandemic and bring these perspectives into policy conversations. Their success hinged on their hyper-local knowledge and their unswerving focus on providing real-time evidence to advocate for the wellbeing of affected communities.
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Mattock, Ruth. The Power project: a report on TABLE's exploration of power in the food system. TABLE, 2024. https://doi.org/10.56661/d98edcaf.
Full textAbstract:
At TABLE, we select annual themes to guide our work. These are usually concepts that act as fault lines in discussions of food system transformation, and concern what a ‘good’ food future might look like. Through a series of reports, essays, podcasts, events and explainers we consider the concept from many different angles. We hope that the totality of this work helps reveal the range of values, assumptions and evidence that shape stakeholders’ views and illuminates how and why they may disagree. TABLE’s report at the close of our SCALE theme noted that power was at the root of many concerns about localised or globalised food system approaches. Power is of course a too-big topic, encompassing not only its operation, mechanisms, handlers and impacts, but also what it is and how it is to be identified and redistributed. We approach the concept from multiple angles and via diverse modes of analysis to give a sense of its multifaceted nature. In a collection of 17 podcasts, TABLE asked contributors from a range of disciplines, professional backgrounds and ideological positions to tell us how they understand power and see its operations in their work. Our essays and blogs expanded on these and offer case studies and personal reflections. Our events gave contributors a chance to interact: An open discussion on power asked how participants see power fitting into conversation, while in Whose knowledge counts speakers asked how power might determine what we take as evidence. Lastly, we considered TABLE’s own experience of power in Process and Power at TABLE. Power can be a slippery concept to evaluate and discuss. To give it some materiality, we took protein as a case study, exploring how power has maintained this ‘charismatic nutrient’ at the centre of ideas about nutrition, development and farming. TABLE’s reports add a historical lens to consider how power has structured cultural understandings of protein when it comes to funding, research and international development strategies and activities in Primed for Power: a short cultural history of protein. The Investment, Power and Protein in Sub Saharan Africa report examined financial investment in protein production in sub-Saharan Africa, reflecting on how those cultural narratives are still informing resource distribution.