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Rozprawy doktorskie na temat „Natural Language Processes (NLP)”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Oliveira, Mirna Fernanda de. "Enfrentamento do problema das divergências de tradução por um sistema de tradução automática : um exercício exploratório /." Araraquara : [s.n.], 2006. http://hdl.handle.net/11449/103605.

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Orientador: Bento Carlos Dias da Silva<br>Banca: Beatriz Nunes de Oliveira Longo<br>Banca: Dirce Charara Monteiro<br>Banca: Gladis Maria de Barcellos Almeida<br>Banca: Heronides Maurílio de Melo Moura<br>Resumo: O objetivo desta tese é desenvolver um estudo lingüístico-computacional exploratório de um problema específico que deve ser enfrentado por sistemas de tradução automática: o problema da divergências de tradução quer de natureza sintática quer de natureza léxico-semântica que se verificam entre pares de sentenças de línguas naturais diferentes. Para isso, fundamenta-se na metodologia de

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Oliveira, Mirna Fernanda de [UNESP]. "Enfrentamento do problema das divergências de tradução por um sistema de tradução automática: um exercício exploratório." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/103605.

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Made available in DSpace on 2014-06-11T19:32:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-04-25Bitstream added on 2014-06-13T20:43:58Z : No. of bitstreams: 1 oliveira_mf_dr_ararafcl.pdf: 631650 bytes, checksum: fa4233637c661c5e993adcc08801d158 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>O objetivo desta tese é desenvolver um estudo lingüístico-computacional exploratório de um problema específico que deve ser enfrentado por sistemas de tradução automática: o problema da divergências de tradução quer de natureza sintática quer de natureza léxico

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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 see

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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

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Shao, Han. "Pretraining Deep Learning Models for Natural Language Understanding." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin158955297757398.

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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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<p>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 researc

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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<br>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 me

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Panesar, Kulvinder. "Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions." Universitat Politécnica de Valéncia, 2020. http://hdl.handle.net/10454/18121.

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yes<br>This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers beca

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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 ho

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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 pr

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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<br>Ph.D.<br>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 fr

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Karlin, Ievgen. "An Evaluation of NLP Toolkits for Information Quality Assessment." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-22606.

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Documentation is often the first source, which can help user to solve problems or provide conditions of use of some product. That is why it should be clear and understandable. But what does “understandable” mean? And how to detect whether some text is unclear? And this thesis can answer on those questions.The main idea of current work is to measure clarity of the text information using natural language processing capabilities. There are three global steps to achieve this goal: to define criteria of bad clarity of text information, to evaluate different natural language toolkits and find suitab

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Schreiber, Marc [Verfasser]. "Towards Effective Natural Language Application Development : Foundations of NLP Lean Programming framework / Marc Schreiber." Kassel : Universitätsbibliothek Kassel, 2019. http://d-nb.info/1190045737/34.

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Pfeil, Jonathan W. "Algorithms and Resources for Scalable Natural Language Generation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1465469914.

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Lager, Adam. "Improving Solr search with Natural Language Processing : An NLP implementation for information retrieval in Solr." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177790.

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The field of AI is emerging fast and institutions and companies are pushing the limits of impossibility. Natural Language Processing is a branch of AI where the goal is to understand human speech and/or text. This technology is used to improve an inverted index,the full text search engine Solr. Solr is open source and has integrated OpenNLP makingit a suitable choice for these kinds of operations. NLP-enabled Solr showed great results compared to the Solr that’s currently running on the systems, where NLP-Solr was slightly worse in terms of precision, it excelled at recall and returning the co

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Coppola, Gregory Francis. "Iterative parameter mixing for distributed large-margin training of structured predictors for natural language processing." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10451.

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The development of distributed training strategies for statistical prediction functions is important for applications of machine learning, generally, and the development of distributed structured prediction training strategies is important for natural language processing (NLP), in particular. With ever-growing data sets this is, first, because, it is easier to increase computational capacity by adding more processor nodes than it is to increase the power of individual processor nodes, and, second, because data sets are often collected and stored in different locations. Iterative parameter mixi

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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 informatio

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Ruberg, Nicolaas. "Bert goes sustainable: an NLP approach to ESG financing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24787/.

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Environmental, Social, and Governance (ESG) factors are a strategic topic for investors and financing institutions like the Brazilian Development Bank (BNDES). Currently, the bank’s experts are developing a framework based on those factors to assess companies' sustainable financing. We identify an opportunity to use Natural Language Processing (NLP) in this development. This opportunity arises from the observation that a critical document to the ESG analysis is the company annual activity report. This document undergoes a manual screening, and later it is decomposed, and its parts are redirect

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Azzarone, Andrea. "Evaluating Globally Normalized Transition Based Neural Networks for Multilingual Natural Language Understanding." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208303.

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We analyze globally normalized transition-based neural network models for dependency parsing on English, German, Spanish, and Catalan. We compare the results with FreeLing, an open source language analysis tool developed at the UPC natural language processing research group. Furthermore we study how the mini-batch size, the number of units in the hidden layers and the beam width affect the performances of the network. Finally we propose a multi-lingual parser with parameters sharing and experiment with German and English obtaining a significant accuracy improvement upon the monolingual parsers

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Riedel, Sebastian. "Efficient prediction of relational structure and its application to natural language processing." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4167.

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Many tasks in Natural Language Processing (NLP) require us to predict a relational structure over entities. For example, in Semantic Role Labelling we try to predict the ’semantic role’ relation between a predicate verb and its argument constituents. Often NLP tasks not only involve related entities but also relations that are stochastically correlated. For instance, in Semantic Role Labelling the roles of different constituents are correlated: we cannot assign the agent role to one constituent if we have already assigned this role to another. Statistical Relational Learning (also known as Fir

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Akrin, Christoffer, and Simon Tham. "A Natural Language Interface for Querying Linked Data." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78921.

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The thesis introduces a proof of concept idea that could spark great interest from many industries. The idea consists of a remote Natural Language Interface (NLI), for querying Knowledge Bases (KBs). The system applies natural language technology tools provided by the Stanford CoreNLP, and queries KBs with the use of the query language SPARQL. Natural Language Processing (NLP) is used to analyze the semantics of a question written in natural language, and generates relational information about the question. With correctly defined relations, the question can be queried on KBs containing relevan

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Eriksson, Caroline, and Emilia Kallis. "NLP-Assisted Workflow Improving Bug Ticket Handling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301248.

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Software companies spend a lot of resources on debugging, a process where previous solutions can help in solving current problems. The bug tickets, containing this information, are often time-consuming to read. To minimize the time spent on debugging and to make sure that the knowledge from prior solutions is kept in the company, an evaluation was made to see if summaries could make this process more efficient. Abstractive and extractive summarization models were tested for this task and fine-tuning of the bert-extractive-summarizer was performed. The model-generated summaries were compared in

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Boulanger, Hugo. "Data augmentation and generation for natural language processing." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG019.

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De plus en plus de domaines cherchent à automatiser une partie de leur processus.Le traitement automatique des langues contient des méthodes permettant d'extraire des informations dans des textes.Ces méthodes peuvent utiliser de l'apprentissage automatique.L'apprentissage automatique nécessite des données annotées pour faire de l'extraction d'information de manière optimale.L'application de ces méthodes à de nouveaux domaines nécessite d'obtenir des données annotées liée à la tâche.Le problème que nous souhaitons résoudre est de proposer et d'étudier des méthodes de génération pour améliorer l

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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. Wi

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Demmelmaier, Gustav, and Carl Westerberg. "Data Segmentation Using NLP: Gender and Age." Thesis, Uppsala universitet, Avdelningen för datalogi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-434622.

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Natural language processing (NLP) opens the possibilities for a computer to read, decipher, and interpret human languages to eventually use it in ways that enable yet further understanding of the interaction and communication between the human and the computer. When appropriate data is available, NLP makes it possible to determine not only the sentiment information of a text but also information about the author behind an online post. Previously conducted studies show aspects of NLP potentially going deeper into the subjective information, enabling author classification from text data. This th

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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 a

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Fernquist, Johan. "Detection of deceptive reviews : using classification and natural language processing features." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-306956.

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With the great growth of open forums online where anyone can givetheir opinion on everything, the Internet has become a place wherepeople are trying to mislead others. By assuming that there is acorrelation between a deceptive text's purpose and the way to writethe text, our goal with this thesis was to develop a model fordetecting these fake texts by taking advantage of this correlation.Our approach was to use classification together with threedifferent feature types, term frequency-inverse document frequency,word2vec and probabilistic context-free grammar. We have managed todevelop a model w

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Bhaduri, Sreyoshi. "NLP in Engineering Education - Demonstrating the use of Natural Language Processing Techniques for Use in Engineering Education Classrooms and Research." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82202.

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Engineering Education is a developing field, with new research and ideas constantly emerging and contributing to the ever-evolving nature of this discipline. Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. Analysis of textual data demands consumption of a lot of time and resources. As a result, researchers end up spending a lot of time and effort in analyzing such text repositories. While there is a lot to be gained through in-depth rese

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Kärde, Wilhelm. "Tool for linguistic quality evaluation of student texts." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186434.

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Spell checkers are nowadays a common occurrence in most editors. A student writing an essay in school will often have the availability of a spell checker. However, the feedback from a spell checker seldom correlates with the feedback from a teacher. A reason for this being that the teacher has more aspects on which it evaluates a text. The teacher will, as opposed to the the spell checker, evaluate a text based on aspects such as genre adaptation, structure and word variation. This thesis evaluates how well those aspects translate to NLP (Natural Language Processing) and implements those who t

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Aljadri, Sinan. "Chatbot : A qualitative study of users' experience of Chatbots." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105434.

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The aim of the present study has been to examine users' experience of Chatbot from a business perspective and a consumer perspective. The study has also focused on highlighting what limitations a Chatbot can have and possible improvements for future development. The study is based on a qualitative research method with semi-structured interviews that have been analyzed on the basis of a thematic analysis. The results of the interview material have been analyzed based on previous research and various theoretical perspectives such as Artificial Intelligence (AI), Natural Language Processing (NLP)

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Brockmann, Carsten. "Personality and alignment processes in dialogue : towards a lexically-based unified model." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3980.

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This thesis explores approaches to modelling individual differences in language use. The differences under consideration fall into two broad categories: Variation of the personality projected through language, and modelling of language alignment behaviour between dialogue partners. In a way, these two aspects oppose each other – language related to varying personalities should be recognisably different, while aligning speakers agree on common language during a dialogue. The central hypothesis is that such variation can be captured and produced with restricted computational means. Results from

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Alkathiri, Abdul Aziz. "Decentralized Large-Scale Natural Language Processing Using Gossip Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281277.

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The field of Natural Language Processing in machine learning has seen rising popularity and use in recent years. The nature of Natural Language Processing, which deals with natural human language and computers, has led to the research and development of many algorithms that produce word embeddings. One of the most widely-used of these algorithms is Word2Vec. With the abundance of data generated by users and organizations and the complexity of machine learning and deep learning models, performing training using a single machine becomes unfeasible. The advancement in distributed machine learning

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Hänig, Christian. "Unsupervised Natural Language Processing for Knowledge Extraction from Domain-specific Textual Resources." Doctoral thesis, Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-112706.

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This thesis aims to develop a Relation Extraction algorithm to extract knowledge out of automotive data. While most approaches to Relation Extraction are only evaluated on newspaper data dealing with general relations from the business world their applicability to other data sets is not well studied. Part I of this thesis deals with theoretical foundations of Information Extraction algorithms. Text mining cannot be seen as the simple application of data mining methods to textual data. Instead, sophisticated methods have to be employed to accurately extract knowledge from text which then can b

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Manishina, Elena. "Data-driven natural language generation using statistical machine translation and discriminative learning." Thesis, Avignon, 2016. http://www.theses.fr/2016AVIG0209/document.

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L'humanité a longtemps été passionnée par la création de machines intellectuelles qui peuvent librement intéragir avec nous dans notre langue. Tous les systèmes modernes qui communiquent directement avec l'utilisateur partagent une caractéristique commune: ils ont un système de dialogue à la base. Aujourd'hui pratiquement tous les composants d'un système de dialogue ont adopté des méthodes statistiques et les utilisent largement comme leurs modèles de base. Jusqu'à récemment la génération de langage naturel (GLN) utilisait pour la plupart des patrons/modèles codés manuellement, qui représentai

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Dagerman, Björn. "Semantic Analysis of Natural Language and Definite Clause Grammar using Statistical Parsing and Thesauri." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26142.

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Services that rely on the semantic computations of users’ natural linguistic inputs are becoming more frequent. Computing semantic relatedness between texts is problematic due to the inherit ambiguity of natural language. The purpose of this thesis was to show how a sentence could be compared to a predefined semantic Definite Clause Grammar (DCG). Furthermore, it should show how a DCG-based system could benefit from such capabilities. Our approach combines openly available specialized NLP frameworks for statistical parsing, part-of-speech tagging and word-sense disambiguation. We compute the s

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Giménez, Fayos María Teresa. "Natural Language Processing using Deep Learning in Social Media." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/172164.

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[ES] En los últimos años, los modelos de aprendizaje automático profundo (AP) han revolucionado los sistemas de procesamiento de lenguaje natural (PLN). Hemos sido testigos de un avance formidable en las capacidades de estos sistemas y actualmente podemos encontrar sistemas que integran modelos PLN de manera ubicua. Algunos ejemplos de estos modelos con los que interaccionamos a diario incluyen modelos que determinan la intención de la persona que escribió un texto, el sentimiento que pretende comunicar un tweet o nuestra ideología política a partir de lo que compartimos en redes sociales.

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Marzo, i. Grimalt Núria. "Natural Language Processing Model for Log Analysis to Retrieve Solutions For Troubleshooting Processes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300042.

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In the telecommunications industry, one of the most time-consuming tasks is troubleshooting and the resolution of Trouble Report (TR) tickets. This task involves the understanding of textual data which can be challenging due to its domain- and company-specific features. The text contains many abbreviations, typos, tables as well as numerical information. This work tries to solve the issue of retrieving solutions for new troubleshooting reports in an automated way by using a Natural Language Processing (NLP) model, in particular Bidirectional Encoder Representations from Transformers (BERT)- ba

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Hellmann, Sebastian [Verfasser], Klaus-Peter [Akademischer Betreuer] Fähnrich, Klaus-Peter [Gutachter] Fähnrich, Sören [Akademischer Betreuer] Auer, Jens [Akademischer Betreuer] Lehmann, and Hans [Gutachter] Uszkoreit. "Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data / Sebastian Hellmann ; Gutachter: Klaus-Peter Fähnrich, Hans Uszkoreit ; Klaus-Peter Fähnrich, Sören Auer, Jens Lehmann." Leipzig : Universitätsbibliothek Leipzig, 2015. http://d-nb.info/1239422202/34.

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Lindén, Johannes. "Huvudtitel: Understand and Utilise Unformatted Text Documents by Natural Language Processing algorithms." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31043.

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News companies have a need to automate and make the editors process of writing about hot and new events more effective. Current technologies involve robotic programs that fills in values in templates and website listeners that notifies the editors when changes are made so that the editor can read up on the source change at the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources. This study applies deep learning algorithms to automatically formulate abstracts and tag sources with appropriate tags based on the context. The study

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Storby, Johan. "Information extraction from text recipes in a web format." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189888.

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Searching the Internet for recipes to find interesting ideas for meals to prepare is getting increasingly popular. It can however be difficult to find a recipe for a dish that can be prepared with the items someone has available at home. In this thesis a solution to a part of that problem will be presented. This thesis will investigate a method for extracting the various parts of a recipe from the Internet in order to save them and build a searchable database of recipes where users can search for recipes based on the ingredients they have available. The system works for both English and Swedis

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Nahnsen, Thade. "Automation of summarization evaluation methods and their application to the summarization process." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5278.

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Summarization is the process of creating a more compact textual representation of a document or a collection of documents. In view of the vast increase in electronically available information sources in the last decade, filters such as automatically generated summaries are becoming ever more important to facilitate the efficient acquisition and use of required information. Different methods using natural language processing (NLP) techniques are being used to this end. One of the shallowest approaches is the clustering of available documents and the representation of the resulting clusters by o

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Luff, Robert. "The use of systems engineering principles for the integration of existing models and simulations." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/26739.

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With the rise in computational power, the prospect of simulating a complex engineering system with a high degree of accuracy and in a meaningful way is becoming a real possibility. Modelling and simulation have become ubiquitous throughout the engineering life cycle, as a consequence there are many thousands of existing models and simulations that are potential candidates for integration. This work is concerned with ascertaining if systems engineering principles are of use in the support of virtual testing, from desire to test, designing experiments, specifying simulations, selecting models an

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Alsehaimi, Afnan Abdulrahman A. "Sentiment Analysis for E-book Reviews on Amazon to Determine E-book Impact Rank." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619109972210567.

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Palm, Myllylä Johannes. "Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Speciﬁc Training Data". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693.

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Identifying semantic relations in natural language text is an important component of many knowledge extraction systems. This thesis studies the task of hypernym discovery, i.e discovering terms that are related by the hypernymy (is-a) relation. Speciﬁcally, this thesis explores how state-of-the-art methods for hypernym discovery perform when applied in speciﬁc language domains. In recent times, state-of-the-art methods for hypernym discovery are mostly made up by supervised machine learning models that leverage distributional word representations such as word embeddings. These models require l

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Boukhatem, Moncef. "Natural Language Processing Approaches for Closed-Domain Technical Dialogues." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX151.

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Cette thèse explore diverses méthodes avancées en traitement du langage naturel (TAL) pour améliorer l'interprétation et l'analyse des données textuelles, en particulier dans les dialogues multilingues et techniques. La recherche se concentre sur plusieurs tâches clés du TAL, notamment l'extraction de mots-clés, la modélisation d'arbres syntaxiques, les systèmes de question-réponse techniques, la résolution de cataphore et l'exploitation des données spécifiques à un domaine.Une partie du travail présente de nouvelles approches utilisant des graphes de mots (GoW) et des arbres syntaxiques pour

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Wehbe, Leila. "The Time and Location of Natural Reading Processes in the Brain." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/789.

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How is information organized in the brain during natural reading? Where and when do the required processes occur, such as the perception of individual words and the construction of sentence meanings. How are semantics, syntax and higher-level narrative structure represented? Answering these questions is core to understanding how the brain processes language and organizes complex information. However, due to the complexity of language processing, most brain imaging studies focus only on one of these questions using highly controlled stimuli which may not generalize beyond the experimental setti

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Björkman, Desireé. "Machine Learning Evaluation of Natural Language to Computational Thinking : On the possibilities of coding without syntax." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424269.

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Voice commands are used in today's society to offer services like putting events into a calendar, tell you about the weather and to control the lights at home. This project tries to extend the possibilities of voice commands by improving an earlier proof of concept system that interprets intention given in natural language to program code. This improvement was made by mixing linguistic methods and neural networks to increase accuracy and flexibility of the interpretation of input. A user testing phase was made to conclude if the improvement would attract users to the interface. The results sho

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Rothwell, Clayton D. "Recurrence Quantification Models of Human Conversational Grounding Processes: Informing Natural Language Human-Computer Interaction." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527591081613424.

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Dai, Xiang. "Recognising Biomedical Names: Challenges and Solutions." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25482.

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The growth rate in the amount of biomedical documents is staggering. Unlocking information trapped in these documents can enable researchers and practitioners to operate confidently in the information world. Biomedical Named Entity Recognition (NER), the task of recognising biomedical names, is usually employed as the first step of the NLP pipeline. Standard NER models, based on sequence tagging technique, are good at recognising short entity mentions in the generic domain. However, there are several open challenges of applying these models to recognise biomedical names: ● Biomedical names

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Khizra, Shufa. "Using Natural Language Processing and Machine Learning for Analyzing Clinical Notes in Sickle Cell Disease Patients." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright154759374321405.

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