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Dissertations / Theses on the topic 'Multilingual Word Sense Disambiguation'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Dandala, Bharath. "Multilingual Word Sense Disambiguation Using Wikipedia." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc500036/.

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Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three differe

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2

Tchechmedjiev, Andon. "Interopérabilité Sémantique Multi-lingue des Ressources Lexicales en Données Liées Ouvertes." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM067/document.

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Lorsqu’il s’agit la construction de ressources lexico-sémantiques multilingues, la première chose qui vient à l’esprit, et la nécessité que les ressources à alignées partagent le même format de données et la même représentations (interopérabilité représentationnelle). Avec l’apparition de standard tels que LMF et leur adaptation au web sémantique pour la production de ressources lexico- sémantiques multilingues en tant que données lexicales liées ouvertes (Ontolex), l’interopérabilité représentationnelle n’est plus un verrou majeur. Cependant, en ce qui concerne l’interopérabilité des aligneme

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3

Alexopoulou, Dimitra. "Word-sense disambiguation in biomedical ontologies." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62843.

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With the ever increase in biomedical literature, text-mining has emerged as an important technology to support bio-curation and search. Word sense disambiguation (WSD), the correct identification of terms in text in the light of ambiguity, is an important problem in text-mining. Since the late 1940s many approaches based on supervised (decision trees, naive Bayes, neural networks, support vector machines) and unsupervised machine learning (context-clustering, word-clustering, co-occurrence graphs) have been developed. Knowledge-based methods that make use of the WordNet computational lexicon h

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4

Ciaramita, Massimiliano. "Broad-coverage hierarchical word sense disambiguation /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174590.

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5

Dionisio, Nuno M. F. "Nominal taxonomies and word sense disambiguation." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405219.

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6

Sanderson, Mark. "Word sense disambiguation and information retrieval." Thesis, University of Glasgow, 1996. http://theses.gla.ac.uk/4463/.

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Starting with a review of previous research that attempted to improve the representation of documents in IR systems, this research is reassessed in the light of word sense ambiguity. It will be shown that a number of the attempts' successes or failures were due to the noticing or ignoring of ambiguity. In the review of disambiguation research, many varied techniques for performing automatic disambiguities are introduced. Research on the disambiguating abilities of people is presented also. It has been found that people are inconsistent when asked to disambiguate words and this causes problems

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7

Hawkins, Paul Martin. "DURHAM : a word sense disambiguation system." Thesis, Durham University, 1999. http://etheses.dur.ac.uk/4493/.

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Ever since the 1950's when Machine Translation first began to be developed, word sense disambiguation (WSD) has been considered a problem to developers. In more recent times, all NLP tasks which are sensitive to lexical semantics potentially benefit from WSD although to what extent is largely unknown. The thesis presents a novel approach to the task of WSD on a large scale. In particular a novel knowledge source is presented named contextual information. This knowledge source adopts a sub-symbolic training mechanism to learn information from the context of a sentence which is able to aid disam

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8

Christiansson, Jens, and Zeina Zimmerman. "Word sense disambiguation med Svenskt OrdNät." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18220.

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In information retrieval there is a problem with ambiguous words. To solve this problem word sense disambiguation WSD is used. Few studies combining information retrieval and word sense disambiguation have been conducted with Swedish words. The purpose of this thesis has been divided in two. The first purpose was to examine Swedish information retrieval and disambiguation in the query phase. The second purpose was to compare the disambiguation between automatic and manual expansion. We chose a number of topics from the GP_HDINF test collection in Query Performance Analyser QPA. The topics

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9

Chasin, Rachel (Rachel G. ). "Word sense disambiguation in clinical text." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84878.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 57-59).<br>Lexical ambiguity, the ambiguity arising from a string with multiple meanings, is pervasive in language of all domains. Word sense disambiguation (WSD) and word sense induction (WSI) are the tasks of resolving this ambiguity. Applications in the clinical and biomedical domain focus on the potential disambiguation has for information extraction. Most approaches to the problem ar

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10

Stickgold, Eli (Eli B. ). "Word sense disambiguation through lattice learning." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66811.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 51).<br>The question of how a computer reading a text can go from a word to its meaning is an open and difficult one. The WordNet[3] lexical database uses a system of nested supersets to allow programs to be specific as to what meaning of a word they are using, but a system that picks the correct meaning is still necessary. In an attempt to capture the human understanding of this problem and prod

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11

Baragh, Armin Shams. "A novel model for word sense disambiguation." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527417.

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12

Stevenson, Robert Mark. "Multiple knowledge sources for word sense disambiguation." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310763.

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Carpuat, Marine Jacinthe. "Word sense disambiguation for statistical machine translation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20CARPUA.

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Uzuner, Ozlem 1975. "Word sense disambiguation applied to information retrieval." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9874.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaves 45-46).<br>by Ozlem Uzuner.<br>S.B.and M.Eng.

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15

Stokoe, Christopher. "Automated word sense disambiguation for Web information retrieval." Thesis, University of Sunderland, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408881.

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16

Maraldi, Giulio. "BabelNet e il problema della Word Sense Disambiguation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7231/.

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La Word Sense Disambiguation è un problema informatico appartenente al campo di studi del Natural Language Processing, che consiste nel determinare il senso di una parola a seconda del contesto in cui essa viene utilizzata. Se un processo del genere può apparire banale per un essere umano, può risultare d'altra parte straordinariamente complicato se si cerca di codificarlo in una serie di istruzioni esguibili da una macchina. Il primo e principale problema necessario da affrontare per farlo è quello della conoscenza: per operare una disambiguazione sui termini di un testo, un computer deve pot

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17

Widlak, Magdalena. "Influence of word sense disambiguation on text classification." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26808.

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Word sense ambiguity is a pervasive characteristic of natural language. The discrimination of word senses, word sense disambiguation, is considered to be of prime importance for many areas involving computerized language analysis, from machine translation to information retrieval. Text classification, as a growing subfield of information retrieval, is also believed to suffer from the effects of word sense ambiguity. The purpose of this thesis was to evaluate how word sense disambiguation affects text classification. The intuitive hypothesis is that word sense disambiguation aids the task of te

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18

Miller, Tristan. "Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation." Phd thesis, tuprints, 2016. https://tuprints.ulb.tu-darmstadt.de/5400/7/Miller_thesis_20160411_print.pdf.

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Word sense disambiguation (WSD)—the task of determining which meaning a word carries in a particular context—is a core research problem in computational linguistics. Though it has long been recognized that supervised (machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for delibe

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19

Sinha, Ravi Som Mihalcea Rada F. "Graph-based centrality algorithms for unsupervised word sense disambiguation." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9736.

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Sinha, Ravi Som. "Graph-based Centrality Algorithms for Unsupervised Word Sense Disambiguation." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc9736/.

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This thesis introduces an innovative methodology of combining some traditional dictionary based approaches to word sense disambiguation (semantic similarity measures and overlap of word glosses, both based on WordNet) with some graph-based centrality methods, namely the degree of the vertices, Pagerank, closeness, and betweenness. The approach is completely unsupervised, and is based on creating graphs for the words to be disambiguated. We experiment with several possible combinations of the semantic similarity measures as the first stage in our experiments. The next stage attempts to score in

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21

Wang, Xinglong. "Automatic acquisition of sense examples for word sense disambiguation : a bilingual approach." Thesis, University of Sussex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439620.

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22

Enss, Matthew. "An Investigation of Word Sense Disambiguation for Improving Lexical Chaining." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2938.

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This thesis investigates how word sense disambiguation affects lexical chains, as well as proposing an improved model for lexical chaining in which word sense disambiguation is performed prior to lexical chaining. A lexical chain is a set of words from a document that are related in meaning. Lexical chains can be used to identify the dominant topics in a document, as well as where changes in topic occur. This makes them useful for applications such as topic segmentation and document summarization. <br /><br /> However, polysemous words are an inherent problem for algorithms that f

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Henrich, Verena [Verfasser], and Erhard [Akademischer Betreuer] Hinrichs. "Word Sense Disambiguation with GermaNet / Verena Henrich ; Betreuer: Erhard Hinrichs." Tübingen : Universitätsbibliothek Tübingen, 2015. http://d-nb.info/1163396915/34.

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24

Martelli, Federico. "Word Sense Disambiguation in Tongue2Tongue, a Pioneering Computer-aided Translation Tool." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Over the last years, technology has achieved a dominant position in a wide range of fields, causing a profound paradigm shift in our lives. In the area of translation, technology has brought to light useful applications such as machine translation and computer-aided translation. While the former is aimed at automatically translating texts, the latter is intended to support and facilitate the translation process. In consideration of the remarkable success of translation technology, the present master’s thesis proposes a prototype for an innovative CAT tool calledTongue2Tongue which exploits st

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Preiss, Judita. "Probabilistic word sense disambiguation : analysis and techniques for combining knowledge sources." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613902.

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26

Wu, Xinyu. "Development of a hybrid symbolic/connectionist system for word sense disambiguation." Thesis, University of Ulster, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339308.

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27

Rudnick, Alexander James. "Cross-Lingual Word Sense Disambiguation for Low-Resource Hybrid Machine Translation." Thesis, Indiana University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13422906.

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<p> This thesis argues that cross-lingual word sense disambiguation (CL-WSD) can be used to improve lexical selection for machine translation when translating from a resource-rich language into an under-resourced one, especially when relatively little bitext is available. In CL-WSD, we perform word sense disambiguation, considering the senses of a word to be its possible translations into some target language, rather than using a sense inventory developed manually by lexicographers. </p><p> Using explicitly trained classifiers that make use of source-language context and of resources for the

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Gledson, Ann Lesley. "Measuring Topic Homogeneity and its Application to Dictionary-based Word Sense Disambiguation." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492743.

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The use of topical features is abundant in Natural Language Processing (NLP), a major example being in dictionary-based Word Sense Disambiguation (WSD). Topic features rely on the context of a target word, but although the role of context has been discussed as an 'open problem' in the WSD literature, the nature of context, and how it might vary between different styles of documents has been largely ignored.

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Manion, Steve Lawrence. "Unsupervised Knowledge-based Word Sense Disambiguation: Exploration & Evaluation of Semantic Subgraphs." Thesis, University of Canterbury. Department of Mathematics & Statistics, 2014. http://hdl.handle.net/10092/10016.

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Hypothetically, if you were told: Apple uses the apple as its logo . You would immediately detect two different senses of the word apple , these being the company and the fruit respectively. Making this distinction is the formidable challenge of Word Sense Disambiguation (WSD), which is the subtask of many Natural Language Processing (NLP) applications. This thesis is a multi-branched investigation into WSD, that explores and evaluates unsupervised knowledge-based methods that exploit semantic subgraphs. The nature of research covered by this thesis can be broken down to: 1. Mining data from

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Smith, Kelly. "The Rumble in the Disambiguation Jungle : Towards the comparison of a traditional word sense disambiguation system with a novel paraphrasing system." Thesis, Stockholms universitet, Avdelningen för datorlingvistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-61642.

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Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- semous words in context with their correct meaning, known as a sense. WSD is riddled with various obstacles that must be overcome in order to reach its full potential. One of these problems is the aspect of the representation of word meaning. Traditional WSD algorithms make the assumption that a word in a given context has only one meaning and therfore can return only one discrete sense. On the other hand, a novel approach is that a given word can have multiple senses. Studies on graded word sense

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Faruque, Md Ehsanul. "A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4969/.

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Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is bas

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Tallo, Philip T. "Using Sentence Embeddings for Word Sense Induction." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748873435158.

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33

Nóbrega, Fernando Antônio Asevêdo. "Desambiguação lexical de sentidos para o português por meio de uma abordagem multilíngue mono e multidocumento." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28082013-145948/.

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A ambiguidade lexical é considerada uma das principais barreiras para melhoria de aplicações do Processamento de Língua Natural (PLN). Neste contexto, tem-se a área de Desambiguação Lexical de Sentido (DLS), cujo objetivo é desenvolver e avaliar métodos que determinem o sentido correto de uma palavra em um determinado contexto por meio de um conjunto finito de possíveis significados. A DLS é empregada, principalmente, no intuito de prover recursos e ferramentas para diminuir problemas de ambiguidade e, consequentemente, contribuir para melhorias de resultados em outras áreas do PLN. Para o Por

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Oliveira, Francisco de. "Unsupervised Word Sense Disambiguation using non-aligned bilingual corpus in application to Portuguese-Chinese Machine Translation." Thesis, University of Macau, 2006. http://umaclib3.umac.mo/record=b1636970.

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Montanari, Davide. "Semantically-aware text generation: an approach based on deep learning, word sense disambiguation and linguistic frames." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The objective of this thesis is to blend state-of-the-art neural architectures with the still scarcely exploited potential of symbolic knowledge bases, to contribute tackling one of the biggest open problems in artificial intelligence in a specific subfield of natural language processing (NLP): commonsense reasoning in text generation. Given a set of concepts (expressed by nouns and verbs), the goal is to generate a short sentence that acts as a description of a scene, plausible according to human commonsense knowledge. This problem can be seen as a special case of constrained text generation,

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Miller, Tristan [Verfasser], Iryna [Akademischer Betreuer] Gurevych, Rada [Akademischer Betreuer] Mihalcea, and Wolf-Tilo [Akademischer Betreuer] Balke. "Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation / Tristan Miller. Betreuer: Iryna Gurevych ; Rada Mihalcea ; Wolf-Tilo Balke." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2016. http://d-nb.info/1112142355/34.

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Soriano-Morales, Edmundo-Pavel. "Hypergraphs and information fusion for term representation enrichment : applications to named entity recognition and word sense disambiguation." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2009/document.

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Donner du sens aux données textuelles est une besoin essentielle pour faire les ordinateurs comprendre notre langage. Pour extraire des informations exploitables du texte, nous devons les représenter avec des descripteurs avant d’utiliser des techniques d’apprentissage. Dans ce sens, le but de cette thèse est de faire la lumière sur les représentations hétérogènes des mots et sur la façon de les exploiter tout en abordant leur nature implicitement éparse.Dans un premier temps, nous proposons un modèle de réseau basé sur des hypergraphes qui contient des données linguistiques hétérogènes dans u

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Hasler, Eva Cornelia. "Dynamic topic adaptation for improved contextual modelling in statistical machine translation." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10522.

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In recent years there has been an increased interest in domain adaptation techniques for statistical machine translation (SMT) to deal with the growing amount of data from different sources. Topic modelling techniques applied to SMT are closely related to the field of domain adaptation but more flexible in dealing with unstructured text. Topic models can capture latent structure in texts and are therefore particularly suitable for modelling structure in between and beyond corpus boundaries, which are often arbitrary. In this thesis, the main focus is on dynamic translation model adaptation to

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Baglodi, Venkatesh. "A Feature Structure Approach for Disambiguating Preposition Senses." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/83.

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Word Sense Disambiguation (WSD) continues to be an open research problem in spite of recent advances in the NLP field, especially in machine learning. WSD for open-class words is well understood. However, WSD for closed class structural words (such as prepositions) is not so well resolved, and their role in frame semantics seems to be a relatively unknown area. This research uses a new method to disambiguate preposition senses by using a combined lookup from FrameNet and TPP databases. Motivated by recent work by Popescu, Tonelli, & Pianta (2007), it extends the concept to provide a determinis

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Martinez, Jorge Matadamas. "AXEL : a framework to deal with ambiguity in three-noun compounds." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4774.

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Cognitive Linguistics has been widely used to deal with the ambiguity generated by words in combination. Although this domain offers many solutions to address this challenge, not all of them can be implemented in a computational environment. The Dynamic Construal of Meaning framework is argued to have this ability because it describes an intrinsic degree of association of meanings, which in turn, can be translated into computational programs. A limitation towards a computational approach, however, has been the lack of syntactic parameters. This research argues that this limitation could be ove

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Wijeratne, Sanjaya. "A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547506375922938.

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Brody, Samuel. "Closing the gap in WSD : supervised results with unsupervised methods." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3981.

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Word-Sense Disambiguation (WSD), holds promise for many NLP applications requiring broad-coverage language understanding, such as summarization (Barzilay and Elhadad, 1997) and question answering (Ramakrishnan et al., 2003). Recent studies have also shown that WSD can benefit machine translation (Vickrey et al., 2005) and information retrieval (Stokoe, 2005). Much work has focused on the computational treatment of sense ambiguity, primarily using data-driven methods. The most accurate WSD systems to date are supervised and rely on the availability of sense-labeled training data. This restricti

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Wallin, Moa. "Ambiguous synonyms : Implementing an unsupervised WSD system for division of synonym clusters containing multiple senses." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157622.

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When clustering together synonyms, complications arise in cases of the words having multiple senses as each sense’s synonyms are erroneously clustered together. The task of automatically distinguishing word senses in cases of ambiguity, known as word sense disambiguation (WSD), has been an extensively researched problem over the years. This thesis studies the possibility of applying an unsupervised machine learning based WSD-system for analysing existing synonym clusters (N = 149) and dividing them correctly when two or more senses are present. Based on sense embeddings induced from a large co

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Wang, Qianqian. "NATURAL LANGUAGE PROCESSING BASED GENERATOR OF TESTING INSTRUMENTS." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/576.

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Natural Language Processing (NLP) is the field of study that focuses on the interactions between human language and computers. By “natural language” we mean a language that is used for everyday communication by humans. Different from programming languages, natural languages are hard to be defined with accurate rules. NLP is developing rapidly and it has been widely used in different industries. Technologies based on NLP are becoming increasingly widespread, for example, Siri or Alexa are intelligent personal assistants using NLP build in an algorithm to communicate with people. “Natural Langua

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Ahmady, Tobias, and Rosmar Sander Klein. "Translation of keywords between English and Swedish." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146637.

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In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine trans

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Boynuegri, Akif. "Cross-lingual Information Retrieval On Turkish And English Texts." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611903/index.pdf.

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In this thesis, cross-lingual information retrieval (CLIR) approaches are comparatively evaluated for Turkish and English texts. As a complementary study, knowledge-based methods for word sense disambiguation (WSD), which is one of the most important parts of the CLIR studies, are compared for Turkish words. Query translation and sense indexing based CLIR approaches are used in this study. In query translation approach, we use automatic and manual word sense disambiguation methods and Google translation service during translation of queries. In sense indexing based approach, documents are inde

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Mendes, Pablo N. "Adaptive Semantic Annotation of Entity and Concept Mentions in Text." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504.

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Cabezudo, Marco Antonio Sobrevilla. "Investigação de métodos de desambiguação lexical de sentidos de verbos do português do Brasil." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07012016-112043/.

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A Desambiguação Lexical de Sentido (DLS) consiste em determinar o sentido mais apropriado da palavra em um contexto determinado, utilizando-se um repositório de sentidos pré-especificado. Esta tarefa é importante para outras aplicações, por exemplo, a tradução automática. Para o inglês, a DLS tem sido amplamente explorada, utilizando diferentes abordagens e técnicas, contudo, esta tarefa ainda é um desafio para os pesquisadores em semântica. Analisando os resultados dos métodos por classes gramaticais, nota-se que todas as classes não apresentam os mesmos resultados, sendo que os verbos são os

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Silva, Viviane Santos da. "Desambiguação automática de substantivos em corpus do português brasileiro." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/8/8139/tde-13122016-131107/.

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Abstract:

O fenômeno da ambiguidade lexical foi o tópico central desta pesquisa, especialmente no que diz respeito às relações entre acepções de formas gráficas ambíguas e aos padrões de distribuição de acepções de palavras polissêmicas na língua, isto é, de palavras cujas acepções são semanticamente relacionadas. Este trabalho situa-se como uma proposta de interface entre explorações computacionais da ambiguidade lexical, especificamente de processamento de linguagem natural, e investigações de cunho teórico sobre o fenômeno do significado lexical. Partimos das noções de polissemia e de homonímia como

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Steinmetz, Nadine. "Context-aware semantic analysis of video metadata." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/7055/.

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Abstract:

Im Vergleich zu einer stichwortbasierten Suche ermöglicht die semantische Suche ein präziseres und anspruchsvolleres Durchsuchen von (Web)-Dokumenten, weil durch die explizite Semantik Mehrdeutigkeiten von natürlicher Sprache vermieden und semantische Beziehungen in das Suchergebnis einbezogen werden können. Eine semantische, Entitäten-basierte Suche geht von einer Anfrage mit festgelegter Bedeutung aus und liefert nur Dokumente, die mit dieser Entität annotiert sind als Suchergebnis. Die wichtigste Voraussetzung für eine Entitäten-zentrierte Suche stellt die Annotation der Dokumente im Archiv

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