Academic literature on the topic 'Multilingual Word Sense Disambiguation'

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Journal articles on the topic "Multilingual Word Sense Disambiguation"

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Ion, Radu, and Dan Tufiş. "Multilingual versus monolingual word sense disambiguation." International Journal of Speech Technology 12, no. 2-3 (2009): 113–24. http://dx.doi.org/10.1007/s10772-009-9053-5.

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Lefever, Els, and Véronique Hoste. "Parallel corpora make sense." International Journal of Corpus Linguistics 19, no. 3 (2014): 333–67. http://dx.doi.org/10.1075/ijcl.19.3.02lef.

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We present a multilingual approach to Word Sense Disambiguation (WSD), which automatically assigns the contextually appropriate sense to a given word. Instead of using a predefined monolingual sense-inventory, we use a language-independent framework by deriving the senses of a given word from word alignments on a multilingual parallel corpus, which we made available for corpus linguistics research. We built five WSD systems with English as the input language and translations in five supported languages (viz. French, Dutch, Italian, Spanish and German) as senses. The systems incorporate both bi
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Scarlini, Bianca, Tommaso Pasini, and Roberto Navigli. "SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8758–65. http://dx.doi.org/10.1609/aaai.v34i05.6402.

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Contextual representations of words derived by neural language models have proven to effectively encode the subtle distinctions that might occur between different meanings of the same word. However, these representations are not tied to a semantic network, hence they leave the word meanings implicit and thereby neglect the information that can be derived from the knowledge base itself. In this paper, we propose SensEmBERT, a knowledge-based approach that brings together the expressive power of language modelling and the vast amount of knowledge contained in a semantic network to produce high-q
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Moro, Andrea, Alessandro Raganato, and Roberto Navigli. "Entity Linking meets Word Sense Disambiguation: a Unified Approach." Transactions of the Association for Computational Linguistics 2 (December 2014): 231–44. http://dx.doi.org/10.1162/tacl_a_00179.

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Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coheren
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NAROUEI, MASOUD, MANSOUR AHMADI, and ASHKAN SAMI. "SePaS: Word sense disambiguation by sequential patterns in sentences." Natural Language Engineering 21, no. 2 (2013): 251–69. http://dx.doi.org/10.1017/s1351324913000259.

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AbstractAn open problem in natural language processing is word sense disambiguation (WSD). A word may have several meanings, but WSD is the task of selecting the correct sense of a polysemous word based on its context. Proposed solutions are based on supervised and unsupervised learning methods. The majority of researchers in the area focused on choosing proper size of ‘n’ in n-gram that is used for WSD problem. In this research, the concept has been taken to a new level by using variable ‘n’ and variable size window. The concept is based on the iterative patterns extracted from the text. We s
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Tchechmedjiev, Andon, Jérôme Goulian, and Didier Schwab. "Evaluation and Comparison of multilingual fusion strategies for similarity-based Word Sense Disambiguation." Research in Computing Science 70, no. 1 (2013): 69–80. http://dx.doi.org/10.13053/rcs-70-1-6.

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Bovi, Claudio Delli, and Roberto Navigli. "Multilingual semantic dictionaries for natural language processing: The case of BabelNet." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630015. http://dx.doi.org/10.1142/s2425038416300159.

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Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. A complete semantic model of language, however, needs first of all reliable building blocks. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced inc
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Edmonds, Philip, and Eneko Agirre. "Word sense disambiguation." Scholarpedia 3, no. 7 (2008): 4358. http://dx.doi.org/10.4249/scholarpedia.4358.

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Navigli, Roberto. "Word sense disambiguation." ACM Computing Surveys 41, no. 2 (2009): 1–69. http://dx.doi.org/10.1145/1459352.1459355.

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Turdakov, D. Yu. "Word sense disambiguation methods." Programming and Computer Software 36, no. 6 (2010): 309–26. http://dx.doi.org/10.1134/s0361768810060010.

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

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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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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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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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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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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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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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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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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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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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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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Books on the topic "Multilingual Word Sense Disambiguation"

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Agirre, Eneko, and Philip Edmonds, eds. Word Sense Disambiguation. Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-4809-8.

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Agirre, Eneko, and Philip Edmonds, eds. Word Sense Disambiguation. Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/1-4020-4809-2.

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Cottrell, Garrison Weeks. A connectionist approach to word sense disambiguation. Pitman, 1989.

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T. Hristea, Florentina. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33693-5.

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Stevenson, Mark. Word sense disambiguation: The case for combinations of knowledge sources. Center for the Study of Language and Information, 2003.

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Word sense disambiguation: The case for combinations of knowledge sources. Center for the Study of Language and Information, 2003.

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Wu, Xinyu. Development of a hybrid symbolic/connectionist system for word sense disambiguation. The Author], 1995.

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Kwong, Oi Yee. New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1320-2.

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Kwong, Oi Yee. New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation. Springer New York, 2013.

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Hristea, Florentina T. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Multilingual Word Sense Disambiguation"

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Martínez-Fernández, J. L., Ana M. García-Serrano, Julio Villena Román, and Paloma Martínez. "Expanding Queries Through Word Sense Disambiguation." In Evaluation of Multilingual and Multi-modal Information Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_74.

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Specia, Lucia, Maria das Graças V. Nunes, and Mark Stevenson. "Exploiting parallel texts to produce a multilingual sense tagged corpus for word sense disambiguation." In Recent Advances in Natural Language Processing IV. John Benjamins Publishing Company, 2007. http://dx.doi.org/10.1075/cilt.292.36spe.

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Paprotté, Wolf. "Word Sense Disambiguation." In Current Issues in Linguistic Theory. John Benjamins Publishing Company, 1998. http://dx.doi.org/10.1075/cilt.171.14pap.

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Dahlgren, Kathleen. "Word Sense Disambiguation." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1075-4_7.

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Mihalcea, Rada. "Word Sense Disambiguation." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_882.

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Hakenberg, Jörg. "Word Sense Disambiguation." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_184.

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Miikkulainen, Risto, and Rada Mihalcea. "Word Sense Disambiguation." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_882.

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Anaya-Sánchez, Henry, Aurora Pons-Porrata, and Rafael Berlanga-Llavori. "Word Sense Disambiguation Based on Word Sense Clustering." In Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11874850_51.

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Bhingardive, Sudha, and Pushpak Bhattacharyya. "Word Sense Disambiguation Using IndoWordNet." In The WordNet in Indian Languages. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1909-8_15.

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Arranz, Victoria, Jordi Atserias, and Mauro Castillo. "Multiwords and Word Sense Disambiguation." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_28.

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Conference papers on the topic "Multilingual Word Sense Disambiguation"

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Barba, Edoardo, Luigi Procopio, Niccolò Campolungo, Tommaso Pasini, and Roberto Navigli. "MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/531.

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The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automati
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Khapra, Mitesh M., Sapan Shah, Piyush Kedia, and Pushpak Bhattacharyya. "Projecting parameters for multilingual word sense disambiguation." In the 2009 Conference. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1699510.1699570.

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Procopio, Luigi, Edoardo Barba, Federico Martelli, and Roberto Navigli. "MultiMirror: Neural Cross-lingual Word Alignment for Multilingual Word Sense Disambiguation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/539.

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Word Sense Disambiguation (WSD), i.e., the task of assigning senses to words in context, has seen a surge of interest with the advent of neural models and a considerable increase in performance up to 80% F1 in English. However, when considering other languages, the availability of training data is limited, which hampers scaling WSD to many languages. To address this issue, we put forward MultiMirror, a sense projection approach for multilingual WSD based on a novel neural discriminative model for word alignment: given as input a pair of parallel sentences, our model -- trained with a low numbe
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Pasini, Tommaso. "The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/687.

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Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets. In this work, we fi
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Patankar, Shreya N., and Satish R. Devane. "Issues in resolving word sense disambiguation for multilingual translation framework." In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017. http://dx.doi.org/10.1109/iccons.2017.8250716.

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Scozzafava, Federico, Marco Maru, Fabrizio Brignone, Giovanni Torrisi, and Roberto Navigli. "Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-demos.6.

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Mizera-Pietraszko, Jolanta, and Tomasz Machalewski. "Extraction of medical terms for word sense disambiguation within multilingual framework." In 2016 Sixth International Conference on Innovative Computing Technology (INTECH). IEEE, 2016. http://dx.doi.org/10.1109/intech.2016.7845125.

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Pengyuan Liu, Yongzeng Xue, Shiqi Li, and Shui Liu. "Minimum Normalized Google Distance for Unsupervised Multilingual Chinese-English Word Sense Disambiguation." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.69.

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Ayetiran, Eniafe Festus, and Guido Boella. "EBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique." In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/s15-2057.

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Moro, Andrea, and Roberto Navigli. "SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking." In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/s15-2049.

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