Relevant bibliographies by topics / Natural Language Processing (NLP)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Natural Language Processing (NLP)'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Natural Language Processing (NLP)"

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Németh, Renáta, and Júlia Koltai. "Natural language processing." Intersections 9, no. 1 (April 26, 2023): 5–22. http://dx.doi.org/10.17356/ieejsp.v9i1.871.

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Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches. In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some potential solutions to the concerns and provide meaningful and diverse examples of its sociological application, most of which are related to research on Eastern European societies. The focus will be on the use of NLP in quantitative text analysis. Solutions are provided concerning how sociological knowledge can be incorporated into the new methods and how the new analytical tools can be evaluated against the principles of traditional quantitative methodology.
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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.
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Rohit Kumar Yadav, Aanchal Madaan, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (April 30, 2024): 083–90. http://dx.doi.org/10.30574/gjeta.2024.19.1.0058.

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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, there are still some challenges in NLP, such as understanding the meaning of words in context and considering cultural differences. Despite these challenges, NLP is being used in many different areas, from healthcare and finance to education and customer service. NLP is transforming the way humans interact with computers and is making it easier to extract important information from large amounts of text.
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Sadiku, Matthew N. O., Yu Zhou, and Sarhan M. Musa. "NATURAL LANGUAGE PROCESSING IN HEALTHCARE." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 39. http://dx.doi.org/10.23956/ijarcsse.v8i5.626.

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Natural language processing (NLP) refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication. Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind. This paper provides a brief introduction on the use of NLP in healthcare.
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Alharbi, Mohammad, Matthew Roach, Tom Cheesman, and Robert S. Laramee. "VNLP: Visible natural language processing." Information Visualization 20, no. 4 (August 13, 2021): 245–62. http://dx.doi.org/10.1177/14738716211038898.

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In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.
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Al-Khalifa, Hend S., Taif AlOmar, and Ghala AlOlyyan. "Natural Language Processing Patents Landscape Analysis." Data 9, no. 4 (March 31, 2024): 52. http://dx.doi.org/10.3390/data9040052.

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Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.
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Sulistyo, Danang, Fadhli Ahda, and Vivi Aida Fitria. "Epistomologi dalam Natural Language Processing." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 9 (September 26, 2021): 652–64. http://dx.doi.org/10.17977/um068v1i92021p652-664.

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How to obtain the truth about knowledge by considering the axiology and anthology aspects of knowledge is the challenge that epistemology must solve. While in scientific epistemology, the accumulation of information that is true will affect how inquiries about the universe are answered heuristically and how natural occurrences are predicted. The primary goal and aim of epistemology, a subfield of philosophy of science, is to investigate and ascertain the nature of knowledge. As such, it examines the origin, sources, and importance of validity from knowledge in addition to discussing the extent and veracity of science. The goal of NLP, a branch of artificial intelligence (AI), is to enable computers to comprehend human language. For instance, text and voice, which people frequently utilize in casual discussions. Integrating computational linguistics with predictive methods led to the development of NLP. NLP has so far done well with text and audio data. There are still others who believe that NLP is in decline, particularly when it comes to managing idioms and sarcasm in contextual data. Due to the vast number of local languages spoken worldwide, the millions of words they contain, the hundreds of regional accents, and their importance in preventing the extinction of local languages, even machine translation, which was the initial purpose of NLP, may still be investigated further. Masalah yang harus dihadapi oleh Epistomologi adalah bagaimana mendapatkan kebenaran akan pengetahuan dengan menimbang aspek antologi dan aksiologi pada pengetahuan. Sedangkan pada epistomologi ilmiah, penyusunan kebenaran suatu pengetahuan akan berpengaruh untuk menjawab pertanyaan di dunia secara heuristis serta dalam memprediksi fenomena alam yang terjadi. Mempelajari dan menentukan hakikat dari suatu pengetahuan adalah fungsi dan tugas utama epistomologi sebagai salah satu cabang dari filsafat ilmu, maka tidak hanya berbicara tentang kebenaran ilmu pengetahuan dan ruang lingkup pengetahuan, akan tetapi secara luas epistomologi juga mempelajari tentang asal mula, sumber dan juga nilai validitas dari pengetahuan. Pemrosesan bahasa alami, atau NLP, adalah bagian dari kecerdasan buatan (AI) yang berkaitan dengan memberi komputer kemampuan untuk memahami bahasa alami manusia. Misalnya teks dan suara yang sering digunakan manusia dalam percakapan sehari-hari. NLP dibuat dengan menggabungkan linguistik komputasi dengan model statistic. Sampai saat ini NLP memiliki performa yang baik pada data teks dan audio. Namun, masih ada orang yang menilai penurunan dunia NLP, terutama dalam penanganan sarkasme dan idiom dalam data kontekstual. Bahkan terjemahan mesin yang merupakan tujuan awal NLP masih dapat dieksplorasi lebih dalam, karena ada banyak bahasa lokal di dunia, ada jutaan kata, ratusan aksen lokal, dan perannya untuk menyelamatkan Bahasa Lokal dari kepunahan.
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Putri, Nastiti Susetyo Fanany, Prasetya Widiharso, Agung Bella Putra Utama, Maharsa Caraka Shakti, and Urvi Ghosh. "Natural Language Processing in Higher Education." Bulletin of Social Informatics Theory and Application 6, no. 1 (July 3, 2023): 90–101. http://dx.doi.org/10.31763/businta.v6i1.593.

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The application of Natural Language Processing (NLP) in an educational institution is still quite broad in its scope of use, including the use of NLP on chatterbots for academic consultations, handling service dissatisfaction, and spam email detection. Meanwhile, other uses that have not been widely used are the combination of NLP and Global Positioning Satellite (GPS) in finding the location of lecture buildings and other facilities in universities. The combination of NLP and GPS is expected to make it easier for new students, as well as visitors from outside the university, to find the targeted building and facilities more effectively.
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Geetha, Dr V., Dr C. K. Gomathy, Mr P. V. Sri Ram, and Surya Prakash L N. "NOVEL STUDY ON NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27091.

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Natural Language Processing (NLP) is the tech wiz working tirelessly to break down language barriers between us and our devices. It's the reason our smart phone, tablet or laptop understands our voice commands and translates our languages in a second. NLP is like giving machines the ability to comprehend and respond to language nuances, turning our interactions into seamless conversations. Think of it as the digital polyglot that not only reads but truly understands the messages we convey, from the simplest text to the most intricate emotions. From predictive text to chatbots, NLP is the digital linguist enhancing the way we communicate with our devices, making technology feel more like a conversation with a helpful virtual friend. Keywords: seamless conversations, digital polyglot, predictive text, chatbots, digital linguist, technology communication, virtual friend.
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Zhao, Liping, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo, Muideen A. Ajagbe, Erol-Valeriu Chioasca, and Riza T. Batista-Navarro. "Natural Language Processing for Requirements Engineering." ACM Computing Surveys 54, no. 3 (June 2021): 1–41. http://dx.doi.org/10.1145/3444689.

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Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.
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Dissertations / Theses on the topic "Natural Language Processing (NLP)"

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Hellmann, Sebastian. "Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-157932.

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This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have seen the emergence of numerous approaches in various disciplines concerned with linguistic resources and NLP tools. It is the challenge of our time to store, interlink and exploit this wealth of data accumulated in more than half a century of computational linguistics, of empirical, corpus-based study of language, and of computational lexicography in all its heterogeneity. The vision of the Giant Global Graph (GGG) was conceived by Tim Berners-Lee aiming at connecting all data on the Web and allowing to discover new relations between this openly-accessible data. This vision has been pursued by the Linked Open Data (LOD) community, where the cloud of published datasets comprises 295 data repositories and more than 30 billion RDF triples (as of September 2011). RDF is based on globally unique and accessible URIs and it was specifically designed to establish links between such URIs (or resources). This is captured in the Linked Data paradigm that postulates four rules: (1) Referred entities should be designated by URIs, (2) these URIs should be resolvable over HTTP, (3) data should be represented by means of standards such as RDF, (4) and a resource should include links to other resources. Although it is difficult to precisely identify the reasons for the success of the LOD effort, advocates generally argue that open licenses as well as open access are key enablers for the growth of such a network as they provide a strong incentive for collaboration and contribution by third parties. In his keynote at BNCOD 2011, Chris Bizer argued that with RDF the overall data integration effort can be “split between data publishers, third parties, and the data consumer”, a claim that can be substantiated by observing the evolution of many large data sets constituting the LOD cloud. As written in the acknowledgement section, parts of this thesis has received numerous feedback from other scientists, practitioners and industry in many different ways. The main contributions of this thesis are summarized here: Part I – Introduction and Background. During his keynote at the Language Resource and Evaluation Conference in 2012, Sören Auer stressed the decentralized, collaborative, interlinked and interoperable nature of the Web of Data. The keynote provides strong evidence that Semantic Web technologies such as Linked Data are on its way to become main stream for the representation of language resources. The jointly written companion publication for the keynote was later extended as a book chapter in The People’s Web Meets NLP and serves as the basis for “Introduction” and “Background”, outlining some stages of the Linked Data publication and refinement chain. Both chapters stress the importance of open licenses and open access as an enabler for collaboration, the ability to interlink data on the Web as a key feature of RDF as well as provide a discussion about scalability issues and decentralization. Furthermore, we elaborate on how conceptual interoperability can be achieved by (1) re-using vocabularies, (2) agile ontology development, (3) meetings to refine and adapt ontologies and (4) tool support to enrich ontologies and match schemata. Part II - Language Resources as Linked Data. “Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge Acquisition Spiral” summarize the results of the Linked Data in Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in 2013 and give a preview of the MLOD special issue. In total, five proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP & DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012) and one journal special issue (Multilingual Linked Open Data, MLOD to appear) – have been (co-)edited to create incentives for scientists to convert and publish Linked Data and thus to contribute open and/or linguistic data to the LOD cloud. Based on the disseminated call for papers, 152 authors contributed one or more accepted submissions to our venues and 120 reviewers were involved in peer-reviewing. “DBpedia as a Multilingual Language Resource” and “Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked Data Cloud” contain this thesis’ contribution to the DBpedia Project in order to further increase the size and inter-linkage of the LOD Cloud with lexical-semantic resources. Our contribution comprises extracted data from Wiktionary (an online, collaborative dictionary similar to Wikipedia) in more than four languages (now six) as well as language-specific versions of DBpedia, including a quality assessment of inter-language links between Wikipedia editions and internationalized content negotiation rules for Linked Data. In particular the work described in created the foundation for a DBpedia Internationalisation Committee with members from over 15 different languages with the common goal to push DBpedia as a free and open multilingual language resource. Part III - The NLP Interchange Format (NIF). “NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and “Evaluation and Related Work” constitute one of the main contribution of this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. The core specification is included in and describes which URI schemes and RDF vocabularies must be used for (parts of) natural language texts and annotations in order to create an RDF/OWL-based interoperability layer with NIF built upon Unicode Code Points in Normal Form C. In , classes and properties of the NIF Core Ontology are described to formally define the relations between text, substrings and their URI schemes. contains the evaluation of NIF. In a questionnaire, we asked questions to 13 developers using NIF. UIMA, GATE and Stanbol are extensible NLP frameworks and NIF was not yet able to provide off-the-shelf NLP domain ontologies for all possible domains, but only for the plugins used in this study. After inspecting the software, the developers agreed however that NIF is adequate enough to provide a generic RDF output based on NIF using literal objects for annotations. All developers were able to map the internal data structure to NIF URIs to serialize RDF output (Adequacy). The development effort in hours (ranging between 3 and 40 hours) as well as the number of code lines (ranging between 110 and 445) suggest, that the implementation of NIF wrappers is easy and fast for an average developer. Furthermore the evaluation contains a comparison to other formats and an evaluation of the available URI schemes for web annotation. In order to collect input from the wide group of stakeholders, a total of 16 presentations were given with extensive discussions and feedback, which has lead to a constant improvement of NIF from 2010 until 2013. After the release of NIF (Version 1.0) in November 2011, a total of 32 vocabulary employments and implementations for different NLP tools and converters were reported (8 by the (co-)authors, including Wiki-link corpus, 13 by people participating in our survey and 11 more, of which we have heard). Several roll-out meetings and tutorials were held (e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC 2014). Part IV - The NLP Interchange Format in Use. “Use Cases and Applications for NIF” and “Publication of Corpora using NIF” describe 8 concrete instances where NIF has been successfully used. One major contribution in is the usage of NIF as the recommended RDF mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard and the conversion algorithms from ITS to NIF and back. One outcome of the discussions in the standardization meetings and telephone conferences for ITS 2.0 resulted in the conclusion there was no alternative RDF format or vocabulary other than NIF with the required features to fulfill the working group charter. Five further uses of NIF are described for the Ontology of Linguistic Annotations (OLiA), the RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and visualisations of NIF using the RelFinder tool. These 8 instances provide an implemented proof-of-concept of the features of NIF. starts with describing the conversion and hosting of the huge Google Wikilinks corpus with 40 million annotations for 3 million web sites. The resulting RDF dump contains 477 million triples in a 5.6 GB compressed dump file in turtle syntax. describes how NIF can be used to publish extracted facts from news feeds in the RDFLiveNews tool as Linked Data. Part V - Conclusions. provides lessons learned for NIF, conclusions and an outlook on future work. Most of the contributions are already summarized above. One particular aspect worth mentioning is the increasing number of NIF-formated corpora for Named Entity Recognition (NER) that have come into existence after the publication of the main NIF paper Integrating NLP using Linked Data at ISWC 2013. These include the corpora converted by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP Interchange Format for Open German Governmental Data, N^3 – A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format and Global Intelligent Content: Active Curation of Language Resources using Linked Data as well as an early implementation of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr. Further funding for the maintenance, interlinking and publication of Linguistic Linked Data as well as support and improvements of NIF is available via the expiring LOD2 EU project, as well as the CSA EU project called LIDER, which started in November 2013. Based on the evidence of successful adoption presented in this thesis, we can expect a decent to high chance of reaching critical mass of Linked Data technology as well as the NIF standard in the field of Natural Language Processing and Language Resources.
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NOZZA, DEBORA. "Deep Learning for Feature Representation in Natural Language Processing." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2018. http://hdl.handle.net/10281/241185.

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La mole di dati generata dagli utenti sul Web è esponenzialmente cresciuta negli ultimi dieci anni, creando nuove e rilevanti opportunità per ogni tipo di dominio applicativo. Per risolvere i problemi derivanti dall’eccessiva quantità di dati, la ricerca nell’ambito dell’elaborazione del linguaggio naturale si è mossa verso lo sviluppo di modelli computazionali capaci di capirlo ed interpretarlo senza (o quasi) alcun intervento umano. Recentemente, questo campo di studi è stato testimone di un incremento sia in termini di efficienza computazionale che di risultati, per merito dell’avvento di una nuova linea di ricerca nell’apprendimento automatico chiamata Deep Learning. Questa tesi si focalizza in modo particolare su una specifica classe di modelli di Deep Learning atta ad apprendere rappresentazioni di alto livello, e conseguentemente più significative, dei dati di input in ambiente non supervisionato. Nelle tecniche di Deep Learning, queste rappresentazioni sono ottenute tramite multiple trasformazioni non lineari di complessità e astrazione crescente a partire dai dati di input. Questa fase, in cui vengono elaborate le sopracitate rappresentazioni, è un processo cruciale per l’elaborazione del linguaggio naturale in quanto include la procedura di trasformazione da simboli discreti (es. lettere) a una rappresentazione vettoriale che può essere facilmente trattata da un elaboratore. Inoltre, questa rappresentazione deve anche essere in grado di codificare la sintattica e la semantica espressa nel linguaggio utilizzato nei dati. La prima direzione di ricerca di questa tesi mira ad evidenziare come i modelli di elaborazione del linguaggio naturale possano essere potenziati dalle rappresentazioni ottenute con metodi non supervisionati di Deep Learning al fine di conferire un senso agli ingenti contenuti generati dagli utenti. Nello specifico, questa tesi si focalizza su diversi ambiti che sono considerati cruciali per capire di cosa il testo tratti (Named Entity Recognition and Linking) e qual è l’opinione che l’utente sta cercando di esprimere considerando la possibile presenza di ironia (Sentiment Analysis e Irony Detection). Per ognuno di questi ambiti, questa tesi propone modelli innovativi di elaborazione del linguaggio naturale potenziati dalla rappresentazione ottenuta tramite metodi di Deep Learning. Come seconda direzione di ricerca, questa tesi ha approfondito lo sviluppo di un nuovo modello di Deep Learning per l’apprendimento di rappresentazioni significative del testo ulteriormente valorizzato considerando anche la struttura relazionale che sta alla base dei contenuti generati sul Web. Il processo di inferenza terrà quindi in considerazione sia il testo dei dati di input che la componente relazionale sottostante. La rappresentazione, dopo essere stata ottenuta, potrà quindi essere utilizzata da modelli di apprendimento automatico standard per poter eseguire svariate tipologie di analisi nell'ambito di elaborazione del linguaggio naturale. Concludendo, gli studi sperimentali condotti in questa tesi hanno rilevato che l’utilizzo di rappresentazioni più significative ottenute con modelli di Deep Learning, associate agli innovativi modelli di elaborazione del linguaggio naturale proposti in questa tesi, porta ad un miglioramento dei risultati ottenuti e a migliori le abilità di generalizzazione. Ulteriori progressi sono stati anche evidenziati considerando modelli capaci di sfruttare, oltre che al testo, la componente relazionale.
The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. To overcome the difficulties of dealing with this large volume of unstructured data, the research field of Natural Language Processing has provided efficient solutions developing computational models able to understand and interpret human natural language without any (or almost any) human intervention. This field has gained in further computational efficiency and performance from the advent of the recent machine learning research lines concerned with Deep Learning. In particular, this thesis focuses on a specific class of Deep Learning models devoted to learning high-level and meaningful representations of input data in unsupervised settings, by computing multiple non-linear transformations of increasing complexity and abstraction. Indeed, learning expressive representations from the data is a crucial step in Natural Language Processing, because it involves the transformation from discrete symbols (e.g. characters) to a machine-readable representation as real-valued vectors, which should encode semantic and syntactic meanings of the language units. The first research direction of this thesis is aimed at giving evidence that enhancing Natural Language Processing models with representations obtained by unsupervised Deep Learning models can significantly improve the computational abilities of making sense of large volume of user-generated text. In particular, this thesis addresses tasks that were considered crucial for understanding what the text is talking about, by extracting and disambiguating the named entities (Named Entity Recognition and Linking), and which opinion the user is expressing, dealing also with irony (Sentiment Analysis and Irony Detection). For each task, this thesis proposes a novel Natural Language Processing model enhanced by the data representation obtained by Deep Learning. As second research direction, this thesis investigates the development of a novel Deep Learning model for learning a meaningful textual representation taking into account the relational structure underlying user-generated content. The inferred representation comprises both textual and relational information. Once the data representation is obtained, it could be exploited by off-the-shelf machine learning algorithms in order to perform different Natural Language Processing tasks. As conclusion, the experimental investigations reveal that models able to incorporate high-level features, obtained by Deep Learning, show significant performance and improved generalization abilities. Further improvements can be also achieved by models able to take into account the relational information in addition to the textual content.
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Panesar, Kulvinder. "Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective." Vernon Press, 2020. http://hdl.handle.net/10454/18140.

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Yes
This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.
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4

Välme, Emma, and Lea Renmarker. "Accelerating Sustainability Report Assessment with Natural Language Processing." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445912.

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Corporations are expected to be transparent on their sustainability impact and keep their stakeholders informed about how large the impact on the environment is, as well as their work on reducing the impact in question. The transparency is accounted for in a, usually voluntary, sustainability report additional to the already required financial report. With new regulations for mandatory sustainability reporting in Sweden, comprehensive and complete guidelines for corporations to follow are insufficient and the reports tend to be extensive. The reports are therefore hard to assess in terms of how well the reporting is actually done. The Sustainability Reporting Maturity Grid (SRMG) is an assessment tool introduced by Cöster et al. (2020) used for assessing the quality of sustainability reporting. Today, the assessment is performed manually which has proven to be both time-consuming and resulting in varying assessments, affected by individual interpretation of the content. This thesis is exploring how assessment time and grading with the SRMG can be improved by applying Natural Language Processing (NLP) on sustainability documents, resulting in a compressed assessment method - The Prototype. The Prototype intends to facilitate and speed up the process of assessment. The first step towards developing the Prototype was to decide which one of the three Machine Learning models; Naïve Bayes (NB), Support Vector Machines (SVM), or Bidirectional Encoder Representations of Transformers (BERT), is most suitable. This decision was supported by analyzing the accuracy for each model and for respective criteria in the SRMG, where BERT proved a strong classification ability with an average accuracy of 96,8%. Results from the user evaluation of the Prototypeindicated that the assessment time can be halved using the Prototype, with an initial average of 40 minutes decreased to 20 minutes. However, the results further showed a decreased average grading and an increased variation in assessment. The results indicate that applying NLP could be successful, but to get a more competitive Prototype, a more nuanced dataset must be developed, giving more space for the model to detect patterns in the data.
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Djoweini, Camran, and Henrietta Hellberg. "Approaches to natural language processing in app development." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230167.

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Natural language processing is an on-going field that is not yet fully established. A high demand for natural language processing in applications creates a need for good development-tools and different implementation approaches developed to suit the engineers behind the applications. This project approaches the field from an engineering point of view to research approaches, tools, and techniques that are readily available today for development of natural language processing support. The sub-area of information retrieval of natural language processing was examined through a case study, where prototypes were developed to get a deeper understanding of the tools and techniques used for such tasks from an engineering point of view. We found that there are two major approaches to developing natural language processing support for applications, high-level and low-level approaches. A categorization of tools and frameworks belonging to the two approaches as well as the source code, documentation and, evaluations, of two prototypes developed as part of the research are presented. The choice of approach, tools and techniques should be based on the specifications and requirements of the final product and both levels have their own pros and cons. The results of the report are, to a large extent, generalizable as many different natural language processing tasks can be solved using similar solutions even if their goals vary.
Datalingvistik (engelska natural language processing) är ett område inom datavetenskap som ännu inte är fullt etablerat. En hög efterfrågan av stöd för naturligt språk i applikationer skapar ett behov av tillvägagångssätt och verktyg anpassade för ingenjörer. Detta projekt närmar sig området från en ingenjörs synvinkel för att undersöka de tillvägagångssätt, verktyg och tekniker som finns tillgängliga att arbeta med för utveckling av stöd för naturligt språk i applikationer i dagsläget. Delområdet ‘information retrieval’ undersöktes genom en fallstudie, där prototyper utvecklades för att skapa en djupare förståelse av verktygen och teknikerna som används inom området. Vi kom fram till att det går att kategorisera verktyg och tekniker i två olika grupper, beroende på hur distanserad utvecklaren är från den underliggande bearbetningen av språket. Kategorisering av verktyg och tekniker samt källkod, dokumentering och utvärdering av prototyperna presenteras som resultat. Valet av tillvägagångssätt, tekniker och verktyg bör baseras på krav och specifikationer för den färdiga produkten. Resultaten av studien är till stor del generaliserbara eftersom lösningar till många problem inom området är likartade även om de slutgiltiga målen skiljer sig åt.
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6

Sætre, Rune. "GeneTUC: Natural Language Understanding in Medical Text." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-545.

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Natural Language Understanding (NLU) is a 50 years old research field, but its application to molecular biology literature (BioNLU) is a less than 10 years old field. After the complete human genome sequence was published by Human Genome Project and Celera in 2001, there has been an explosion of research, shifting the NLU focus from domains like news articles to the domain of molecular biology and medical literature. BioNLU is needed, since there are almost 2000 new articles published and indexed every day, and the biologists need to know about existing knowledge regarding their own research. So far, BioNLU results are not as good as in other NLU domains, so more research is needed to solve the challenges of creating useful NLU applications for the biologists.

The work in this PhD thesis is a “proof of concept”. It is the first to show that an existing Question Answering (QA) system can be successfully applied in the hard BioNLU domain, after the essential challenge of unknown entities is solved. The core contribution is a system that discovers and classifies unknown entities and relations between them automatically. The World Wide Web (through Google) is used as the main resource, and the performance is almost as good as other named entity extraction systems, but the advantage of this approach is that it is much simpler and requires less manual labor than any of the other comparable systems.

The first paper in this collection gives an overview of the field of NLU and shows how the Information Extraction (IE) problem can be formulated with Local Grammars. The second paper uses Machine Learning to automatically recognize protein name based on features from the GSearch Engine. In the third paper, GSearch is substituted with Google, and the task in this paper is to extract all unknown names belonging to one of 273 biomedical entity classes, like genes, proteins, processes etc. After getting promising results with Google, the fourth paper shows that this approach can also be used to retrieve interactions or relationships between the named entities. The fifth paper describes an online implementation of the system, and shows that the method scales well to a larger set of entities.

The final paper concludes the “proof of concept” research, and shows that the performance of the original GeneTUC NLU system has increased from handling 10% of the sentences in a large collection of abstracts in 2001, to 50% in 2006. This is still not good enough to create a commercial system, but it is believed that another 40% performance gain can be achieved by importing more verb templates into GeneTUC, just like nouns were imported during this work. Work has already begun on this, in the form of a local Masters Thesis.

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7

Andrén, Samuel, and William Bolin. "NLIs over APIs : Evaluating Pattern Matching as a way of processing natural language for a simple API." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186429.

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This report explores of the feasibility of using pattern matching for implementing a robust Natural Language Interface (NLI) over a limited Application Programming Interface (API). Because APIs are used to such a great extent today and often in mobile applications, it becomes more important to find simple ways of making them accessible to end users. A very intuitive way to access information via an API is using natural language. Therefore, this study first explores the possibility of building a corpus of the most common phrases used for a particular API. It is then explored how those phrases adhere to patterns, and how these patterns can be used to extract meaning from a phrase. Finally it evaluates an implementation of an NLI using pattern matching system based on the patterns. The result of the building of the corpus shows that although the amount of unique phrases used with our API seems to increase quite steadily, the amount of patterns those phrases follow converges to a constant quickly. This implies that it is possible to use these patterns to create an NLI that is robust enough to query an API effectively. The evaluation of the pattern matching system indicates that this technique can be used to successfully extract information from a phrase if its pattern is known by the system.
Den här rapporten utforskar hur genomförbart det är att använda mönstermatchning för att implementera ett robust användargränssnitt för styrning med naturligt språk (Natural Language Interface, NLI) över en begränsad Application Programming Interface (API). Eftersom APIer används i stor utsträckning idag, ofta i mobila applikationer, har det blivit allt mer viktigt att hitta sätt att göra dem ännu mer tillgängliga för slutanvändare. Ett mycket intuitivt sätt att komma åt information är med hjälp av naturligt språk via en API. I den här rapporten redogörs först för möjligheten att bygga ett korpus för en viss API and att skapa mönster för mönstermatchning på det korpuset. Därefter utvärderas en implementation av ett NLI som bygger på mönstermatchning med hjälp av korpuset. Resultatet av korpusuppbyggnaden visar att trots att antalet unika fraser som används för vårt API ökar ganska stadigt, så konvergerar antalat mönster på de fraserna relativt snabbt mot en konstant. Detta antyder att det är mycket möjligt att använda desssa mönster för att skapa en NLI som är robust nog för en API. Utvärderingen av implementationen av mönstermatchingssystemet antyder att tekniken kan användas för att framgångsrikt extrahera information från fraser om mönstret frasen följer finns i systemet.
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8

Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.

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Pharmacovigilance, also referred to as drug safety, is an important science for identifying risks related to medicine intake. Side effects of medicine can be caused by for example interactions, high dosage and misuse. In order to find patterns in what causes the unwanted effects, information needs to be gathered and mapped to predefined terms. This mapping is today done manually by experts which can be a very difficult and time consuming task. In this thesis the aim is to automate the process of mapping side effects by using machine learning techniques. The model was developed using information from preexisting mappings of verbatim expressions of side effects. The final model that was constructed made use of the pre-trained language model BERT, which has received state-of-the-art results within the NLP field. When evaluating on the test set the final model performed an accuracy of 80.21%. It was found that some verbatims were very difficult for our model to classify mainly because of ambiguity or lack of information contained in the verbatim. As it is very important for the mappings to be done correctly, a threshold was introduced which left for manual mapping the verbatims that were most difficult to classify. This process could however still be improved as suggested terms were generated from the model, which could be used as support for the specialist responsible for the manual mapping.
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Woldemariam, Yonas Demeke. "Natural language processing in cross-media analysis." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-147640.

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A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Within the architecture of such frameworks, Natural Language Processing (NLP) infrastructures cover a substantial part. The NLP infrastructures include text analysis components such as a parser, named entity extraction and linking, sentiment analysis and automatic speech recognition. Since NLP tools and techniques are originally designed to operate in isolation, integrating them in cross-media frameworks and analyzing textual data extracted from multimedia sources is very challenging. Especially, the text extracted from audio-visual content lack linguistic features that potentially provide important clues for text analysis components. Thus, there is a need to develop various techniques to meet the requirements and design principles of the frameworks. In our thesis, we explore developing various methods and models satisfying text and speech analysis requirements posed by cross-media analysis frameworks. The developed methods allow the frameworks to extract linguistic knowledge of various types and predict various information such as sentiment and competence. We also attempt to enhance the multilingualism of the frameworks by designing an analysis pipeline that includes speech recognition, transliteration and named entity recognition for Amharic, that also enables the accessibility of Amharic contents on the web more efficiently. The method can potentially be extended to support other under-resourced languages.
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10

Huang, Fei. "Improving NLP Systems Using Unconventional, Freely-Available Data." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/221031.

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Computer and Information Science
Ph.D.
Sentence labeling is a type of pattern recognition task that involves the assignment of a categorical label to each member of a sentence of observed words. Standard supervised sentence-labeling systems often have poor generalization: it is difficult to estimate parameters for words which appear in the test set, but seldom (or never) appear in the training set, because they only use words as features in their prediction tasks. Representation learning is a promising technique for discovering features that allow a supervised classifier to generalize from a source domain dataset to arbitrary new domains. We demonstrate that features which are learned from distributional representations of unlabeled data can be used to improve performance on out-of-vocabulary words and help the model to generalize. We also argue that it is important for a representation learner to be able to incorporate expert knowledge during its search for helpful features. We investigate techniques for building open-domain sentence labeling systems that approach the ideal of a system whose accuracy is high and consistent across domains. In particular, we investigate unsupervised techniques for language model representation learning that provide new features which are stable across domains, in that they are predictive in both the training and out-of-domain test data. In experiments, our best system with the proposed techniques reduce error by as much as 11.4% relative to the previous system using traditional representations on the Part-of-Speech tagging task. Moreover, we leverage the Posterior Regularization framework, and develop an architecture for incorporating biases from prior knowledge into representation learning. We investigate three types of biases: entropy bias, distance bias and predictive bias. Experiments on two domain adaptation tasks show that our biased learners identify significantly better sets of features than unbiased learners. This results in a relative reduction in error of more than 16% for both tasks with respect to existing state-of-the-art representation learning techniques. We also extend the idea of using additional unlabeled data to improve the system's performance on a different NLP task, word alignment. Traditional word alignment only takes a sentence-level aligned parallel corpus as input and generates the word-level alignments. However, as the integration of different cultures, more and more people are competent in multiple languages, and they often use elements of multiple languages in conversations. Linguist Code Switching (LCS) is such a situation where two or more languages show up in the context of a single conversation. Traditional machine translation (MT) systems treat LCS data as noise, or just as regular sentences. However, if LCS data is processed intelligently, it can provide a useful signal for training word alignment and MT models. In this work, we first extract constraints from this code switching data and then incorporate them into a word alignment model training procedure. We also show that by using the code switching data, we can jointly train a word alignment model and a language model using co-training. Our techniques for incorporating LCS data improve by 2.64 in BLEU score over a baseline MT system trained using only standard sentence-aligned corpora.
Temple University--Theses
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Books on the topic "Natural Language Processing (NLP)"

1

Christodoulakis, Dimitris N., ed. Natural Language Processing — NLP 2000. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45154-4.

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International Conference on Natural Language Processing (2nd 2000 Patrai, Greece). Natural language processing - NLP 2000: Second International Conference, Patras, Greece, June 2-4, 2000 : proceedings. Berlin: Springer, 2000.

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Gurevych, Iryna. The People’s Web Meets NLP: Collaboratively Constructed Language Resources. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Chengqing, Zong, Chinese Association for Artificial Intelligence., IEEE Signal Processing Society, IEEE Systems, Man, and Cybernetics Society, and Institute of Electrical and Electronics Engineers. Beijing Section., eds. 2003 International conference on natural language processing and knowledge engineering: Proceedings : NLP-KE 2003 : Beijing, China. Piscataway, New Jersey: IEEE, 2003.

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Oppentocht, Anna Linnea. Lexical semantic classification of Dutch verbs: Towards constructing NLP and human-friendly definitions. Utrecht: LEd, 1999.

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Loftsson, Hrafn, Eiríkur Rögnvaldsson, and Sigrún Helgadóttir, eds. Advances in natural language processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010 : proceedings. Berlin: Springer, 2010.

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Kyoko, Kanzaki, and SpringerLink (Online service), eds. Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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International Conference on Natural Language Processing and Knowledge Engineering (2007 Beijing, China). Proceedings of International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'07) : Aug. 30-Sep. 1, Beijing China. PIscataway, NJ: IEEE, 2007.

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Solution states: A course in solving problems in business with the power of NLP. Bancyfelin, Carmarthen, Wales: Anglo American Book Co., 1996.

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Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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Book chapters on the topic "Natural Language Processing (NLP)"

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Lee, Raymond S. T. "Major NLP Applications." In Natural Language Processing, 199–239. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1999-4_9.

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Steedman, Mark. "Connectionist and symbolist sentence processing." In Natural Language Processing, 95–108. Amsterdam: John Benjamins Publishing Company, 2002. http://dx.doi.org/10.1075/nlp.4.07ste.

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Bunt, Harry, and William Black. "The ABC of Computational Pragmatics." In Natural Language Processing, 1–46. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.01bun.

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Allwood, Jens. "An activity-based approach to pragmatics." In Natural Language Processing, 47–80. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.02all.

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Bunt, Harry. "Dialogue pragmatics and context specification." In Natural Language Processing, 81–149. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.03bun.

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Sabah, Gérard. "Pragmatics in language understanding and cognitively motivated architectures." In Natural Language Processing, 151–88. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.04sab.

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Taylor, Martin M., and David A. Waugh. "Dialogue analysis using layered protocols." In Natural Language Processing, 189–232. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.05tay.

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Redeker, Gisela. "Coherence and structure in text and discourse." In Natural Language Processing, 233–64. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.06red.

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Carter, David. "Discourse focus tracking." In Natural Language Processing, 265–92. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.07car.

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Ramsay, Allan. "Speech act theory and epistemic planning." In Natural Language Processing, 293–310. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.08ram.

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Conference papers on the topic "Natural Language Processing (NLP)"

1

Bianchi, Federico, Debora Nozza, and Dirk Hovy. "Language Invariant Properties in Natural Language Processing." In Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.nlppower-1.9.

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BOUCHEHAM, Anouar. "Natural Language Processing for Social Media Data Mining." In II. Alanya International Congress of Social Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/alanyacongress2-8.

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Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between human languages (text) and computers. It involves preprocessing and analyzing textual data, building language models, and applying algorithms to derive insights and perform language-related tasks which allows transforming the treatment of text data to an intelligent and automatic process. In recent years, online social networking has revolutionized interpersonal communication which has led to the generation of a huge amount of data related to this field. NLP enable computers to understand, interpret, and generate content from various new media sources data in a way that is meaningful and useful. Now, Natural language processing (NLP) is one of the most promising avenues for social media data processing, such as “Sentiment analysis”, “Text classification and topic modelling”, “Named entity recognition”, “Language generation”, “Social network analysis”. Through this research work, we discuss the importance and challenges of using NLP in social studies, especially social networks
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Yin, Kayo, and Malihe Alikhani. "Including Signed Languages in Natural Language Processing (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/753.

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Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in research.
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Alyafeai, Zaid, and Maged Al-Shaibani. "ARBML: Democritizing Arabic Natural Language Processing Tools." In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.nlposs-1.2.

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Gardner, Matt, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. "AllenNLP: A Deep Semantic Natural Language Processing Platform." In Proceedings of Workshop for NLP Open Source Software (NLP-OSS). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-2501.

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E. Samaridi, Nikoletta, Nikitas N. Karanikolas, and Evangelos C. Papakitsos. "Lexicographic Environments in Natural Language Processing (NLP)." In PCI 2020: 24th Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3437120.3437310.

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Mieskes, Margot, Karën Fort, Aurélie Névéol, Cyril Grouin, and Kevin Cohen. "NLP Community Perspectives on Replicability." In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_089.

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Dixon, Anthony, and Daniel Birks. "Improving Policing with Natural Language Processing." In Proceedings of the 1st Workshop on NLP for Positive Impact. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.nlp4posimpact-1.13.

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9

Ellmann, Mathias. "Natural language processing (NLP) applied on issue trackers." In ESEC/FSE '18: 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3283812.3283825.

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Flayeh, Azhar Kassem, Yaser Issam Hamodi, and Nashwan Dheyaa Zaki. "Text Analysis Based on Natural Language Processing (NLP)." In 2022 2nd International Conference on Advances in Engineering Science and Technology (AEST). IEEE, 2022. http://dx.doi.org/10.1109/aest55805.2022.10413039.

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Reports on the topic "Natural Language Processing (NLP)"

1

Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid: Banco de España, August 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering.
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2

Avellán, Leopoldo, and Steve Brito. Crossroads in a Fog: Navigating Latin America's Development Challenges with Text Analytics. Inter-American Development Bank, December 2023. http://dx.doi.org/10.18235/0005489.

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Abstract:
Latin America and the Caribbean are facing challenging times due to a combination of worsening development gaps and limited fiscal space to address them. Furthermore, the region is contending with an unfavorable external environment. Issues such as rising poverty, climate change, inadequate infrastructure, and low-quality education and health services, among others, require immediate attention. Deciding how to prioritize efforts to address these development gaps is challenging due to their complexity and urgency, and setting priorities becomes even more difficult when resources are limited. Therefore, it is crucial to have tools that help policymakers prioritize current development challenges to guide the allocation of financial support from international financial institutions and other development partners. This paper contributes to this discussion by using Natural Language Processing (NLP) to identify the most critical development areas. It applies these techniques to detailed periodic country analysis reports (Country Development Challenges, CDCs) prepared by country economists at the Inter-American Development Bank (IDB) from 2015 to 2021. The study reveals that despite the perception that new development challenges have become more critical lately, the region continues to struggle with the same challenges from the past, particularly those related to the government's institutional capacity, fiscal policy, education, productivity and firms, infrastructure, and poverty.
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Steedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada290396.

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4

Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada603814.

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5

Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada245893.

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6

Firpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15015192.

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7

Anderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada188112.

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8

Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada258837.

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9

Neal, Jeannette G., Elissa L. Feit, Douglas J. Funke, and Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada263301.

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10

Lehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273538.

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