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Journal articles on the topic 'Multilingual Word Sense Disambiguation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Preiss, Judita. "Probabilistic word sense disambiguation." Computer Speech & Language 18, no. 3 (2004): 319–37. http://dx.doi.org/10.1016/j.csl.2004.05.003.

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12

S, Deeksha. "Domain Driven Word Sense Disambiguation." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (2020): 1479–82. http://dx.doi.org/10.22214/ijraset.2020.30599.

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13

Aliwy, Ahmed H., and Hawraa A. Taher. "Word Sense Disambiguation: Survey Study." Journal of Computer Science 15, no. 7 (2019): 1004–11. http://dx.doi.org/10.3844/jcssp.2019.1004.1011.

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14

Zhan, Jing Wen, and Yan Min Chen. "Research on Word Sense Disambiguation." Advanced Materials Research 181-182 (January 2011): 337–42. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.337.

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At present, how to make the computer understand the text message of humanity automatically is a very important issue in computer information technology field. And the problem of word sense disambiguation is a bottleneck of the understanding of natural language. This paper describes the current research situation of word sense disambiguation, introducing its background and application, analyzing various mature approaches, comparing the merits and shortages of those methods, taking a brief introduction of context and international evaluation method of word sense disambiguation and finally making

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15

Ranjan Pal, Alok, and Diganta Saha. "Word Sense Disambiguation: A Survey." International Journal of Control Theory and Computer Modeling 5, no. 3 (2015): 1–16. http://dx.doi.org/10.5121/ijctcm.2015.5301.

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16

McCarthy, Diana. "Word Sense Disambiguation: An Overview." Language and Linguistics Compass 3, no. 2 (2009): 537–58. http://dx.doi.org/10.1111/j.1749-818x.2009.00131.x.

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17

Barnard, Kobus, and Matthew Johnson. "Word sense disambiguation with pictures." Artificial Intelligence 167, no. 1-2 (2005): 13–30. http://dx.doi.org/10.1016/j.artint.2005.04.009.

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18

Vidhu Bhala, R. V., and S. Abirami. "Trends in word sense disambiguation." Artificial Intelligence Review 42, no. 2 (2012): 159–71. http://dx.doi.org/10.1007/s10462-012-9331-5.

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19

Kang, Myung Yun, Bogyum Kim, and Jae Sung Lee. "Word Sense Disambiguation Using Embedded Word Space." Journal of Computing Science and Engineering 11, no. 1 (2017): 32–38. http://dx.doi.org/10.5626/jcse.2017.11.1.32.

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20

Izquierdo, Ruben, Armando Suarez, and German Rigau. "Word vs. Class-Based Word Sense Disambiguation." Journal of Artificial Intelligence Research 54 (September 9, 2015): 83–122. http://dx.doi.org/10.1613/jair.4727.

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As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings r

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21

Zhang, Chun Xiang, Li Li Guo, and Xue Yao Gao. "Word Sense Disambiguation Based on Center Window." Advanced Materials Research 981 (July 2014): 157–60. http://dx.doi.org/10.4028/www.scientific.net/amr.981.157.

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Word sense disambiguation is widely applied to information retrieval, semantic comprehension and automatic summarization. It is an important research problem in natural language processing. In this paper, the center window is determined from the target ambiguous word. The words in the center window are extracted as discriminative features. At the same time, a new method of word sense disambiguation is proposed and the disambiguation classifier is given. The classifier is optimized and tested on SemEval-2007 #Task5 corpus. Experimental results show that the accuracy rate of disambiguation arriv

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22

Fazar, Dika Muhammad, and Nelly Indriani Widiastuti. "Lexical Chain dan Word Sense Disambiguation Untuk Peringkasan Artikel Berbahasa Indonesia." Techno.Com 16, no. 2 (2017): 195–207. http://dx.doi.org/10.33633/tc.v16i2.1413.

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Text Summarization adalah sebuah proses untuk menghasilkan ringkasan suatu dokumen dengan tidak menghilangkan informasi utama dari artikel. Ada beberapa metode untuk melakukan peringkasan, seperti metode rantai leksikal atau lexical chain yang memiliki kinerja yang baik untuk dokumen peringkasan dokumen tunggal. Meskipun demikian penggunaan metode lexical chain ini masih memiliki kelemahan yaitu tidak dapat mengidentifikasi kata yang berambigu dalam pembentukan lexical chain. Dalam penelitian ini, untuk memperbaiki kekurangan tersebut metode lexical chain dilengkapi dengan word sense disambigu

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23

Liu, Yuntong, and Hua Sun. "Word Sense Disambiguation for Chinese Based on Semantics Calculation." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/235096.

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In order to use semantics more effectively in natural language processing, a word sense disambiguation method for Chinese based on semantics calculation was proposed. The word sense disambiguation for a Chinese clause could be achieved by solving the semantic model of the natural language; each step of the word sense disambiguation process was discussed in detail; and the computational complexity of the word sense disambiguation process was analyzed. Finally, some experiments were finished to verify the effectiveness of the method.

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24

Park, Yong-Min, and Jae-Sung Lee. "Word Sense Disambiguation using Korean Word Space Model." Journal of the Korea Contents Association 12, no. 6 (2012): 41–47. http://dx.doi.org/10.5392/jkca.2012.12.06.041.

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25

Gasparri, Luca. "Minimal Semantics and Word Sense Disambiguation." Disputatio 6, no. 39 (2014): 147–71. http://dx.doi.org/10.2478/disp-2014-0011.

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Abstract Emma Borg has defined semantic minimalism as the thesis that the literal content of well-formed declarative sentences is truth-evaluable, fully determined by their lexico-syntactic features, and recoverable by language users with no need to access non-linguistic information. The task of this article is threefold. First, I shall raise a criticism to Borg’s minimalism based on how speakers disambiguate homonymy. Second, I will explore some ways Borg might respond to my argument and maintain that none of them offers a conclusive reply to my case. Third, I shall suggest that in order for

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26

WANG, Rui-Qin, and Fan-Sheng KONG. "Research on Unsupervised Word Sense Disambiguation." Journal of Software 20, no. 8 (2009): 2138–52. http://dx.doi.org/10.3724/sp.j.1001.2009.03566.

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27

Dhungana, Udaya Raj, and Subarna Shakya. "Word Sense Disambiguation using Clue Words." Journal of the Institute of Engineering 10, no. 1 (2014): 192–98. http://dx.doi.org/10.3126/jie.v10i1.10900.

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This paper presents a new model to disambiguate the correct sense of polysemy word based on the related context words for each different sense of the polysemy word. The related context words for each sense are referred to as clue words for the sense. The WordNet organises nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a model that organizes the different senses of polysemy words based on the clue words. These clue words for each sense of a polysemy word are used to dis

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28

REYES, Francis de la C. Fernández, Exiquio C. Pérez LEYVA, and Rogelio Lau FERNáNDEZ. "Word Sense Disambiguation in Information Retrieval." Intelligent Information Management 01, no. 02 (2009): 122–27. http://dx.doi.org/10.4236/iim.2009.12018.

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29

Alian, Marwah, Arafat Awajan Awajan, and Akram Al-Kouz Al-Kouz. "Arabic Word Sense Disambiguation Using Wikipedia." International Journal of Computing and Information Sciences 12, no. 1 (2016): 61–66. http://dx.doi.org/10.21700/ijcis.2016.108.

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30

Elayeb, Bilel. "Arabic word sense disambiguation: a review." Artificial Intelligence Review 52, no. 4 (2018): 2475–532. http://dx.doi.org/10.1007/s10462-018-9622-6.

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31

Banerjee, S., and B. P. Mullick. "Word Sense Disambiguation and WordNet Technology." Literary and Linguistic Computing 23, no. 4 (2008): 505. http://dx.doi.org/10.1093/llc/fqn024.

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32

Basili, Roberto, Michelangelo Della Rocca, and Maria Teresa Pazienza. "Contextual word sense tuning and disambiguation." Applied Artificial Intelligence 11, no. 3 (1997): 235–62. http://dx.doi.org/10.1080/088395197118244.

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33

FLORIAN, RADU, SILVIU CUCERZAN, CHARLES SCHAFER, and DAVID YAROWSKY. "Combining Classifiers for word sense disambiguation." Natural Language Engineering 8, no. 4 (2002): 327–41. http://dx.doi.org/10.1017/s1351324902002978.

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Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naïve Bayes, Cosine, Decision List, Transformation-based Learning and MMVC classifiers. Each classifier has access to the same rich feature space, comprised of distance weighted bag-of-lemmas, local ngram context and specific syntactic relations, such as Verb-Object and N

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34

Jain, Goonjan, and D. K. Lobiyal. "Word sense disambiguation using implicit information." Natural Language Engineering 26, no. 4 (2019): 413–32. http://dx.doi.org/10.1017/s1351324919000421.

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AbstractHumans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous wor

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35

Giyanani, Rohit. "A Survey on Word Sense Disambiguation." IOSR Journal of Computer Engineering 14, no. 6 (2013): 30–33. http://dx.doi.org/10.9790/0661-1463033.

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36

Li, Xiangjun, Song Qing, Huawei Zhang, Tinghua Wang, and Huping Yang. "Kernel methods for word sense disambiguation." Artificial Intelligence Review 46, no. 1 (2015): 41–58. http://dx.doi.org/10.1007/s10462-015-9455-5.

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37

Laorden, Carlos, Igor Santos, Borja Sanz, Gonzalo Alvarez, and Pablo G. Bringas. "Word sense disambiguation for spam filtering." Electronic Commerce Research and Applications 11, no. 3 (2012): 290–98. http://dx.doi.org/10.1016/j.elerap.2011.11.004.

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38

Moldovan, Dan, and Adrian Novischi. "Word sense disambiguation of WordNet glosses." Computer Speech & Language 18, no. 3 (2004): 301–17. http://dx.doi.org/10.1016/j.csl.2004.05.007.

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39

Abed, Saad Adnan, Sabrina Tiun, and Nazlia Omar. "Word sense disambiguation in evolutionary manner." Connection Science 28, no. 3 (2016): 226–41. http://dx.doi.org/10.1080/09540091.2016.1141874.

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40

Rajini, S., and A. Vasuki. "Word sense disambiguation using optimisation techniques." International Journal of Cloud Computing 10, no. 1/2 (2021): 78. http://dx.doi.org/10.1504/ijcc.2021.113986.

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41

Rajini, S., and A. Vasuki. "Word sense disambiguation using optimisation techniques." International Journal of Cloud Computing 10, no. 1/2 (2021): 78. http://dx.doi.org/10.1504/ijcc.2021.10036373.

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42

FEDERICI, STEFANO, SIMONETTA MONTEMAGNI, and VITO PIRRELLI. "SENSE: an analogy-based Word Sense Disambiguation system." Natural Language Engineering 5, no. 2 (1999): 207–18. http://dx.doi.org/10.1017/s135132499900217x.

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The paper describes SENSE, a word sense disambiguation system which makes use of multidimensional analogy-based proportions to infer the most likely sense of a word given its context. Architecture and functioning of the system are illustrated in detail. Results of different experimental settings are given, showing that the system, in spite its conservative bias, successfully copes with the problem of training data sparseness.

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43

Kwong, Oi Yee. "Sense abstractness, semantic activation, and word sense disambiguation." International Journal of Speech Technology 11, no. 3-4 (2008): 135–46. http://dx.doi.org/10.1007/s10772-009-9041-9.

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44

Zhang, Chun Xiang, Xue Yao Gao, and Zhi Mao Lu. "The Algorithm of Sense Disambiguation Based on Bayesian Model." Applied Mechanics and Materials 427-429 (September 2013): 1879–82. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1879.

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Sense disambiguation is an important problem in pattern recognition. In this paper, a new algorithm of sense disambiguation is proposed, in which part-of-speech tags of the left word and the right word around the ambiguous word are extracted as discriminative features. At the same time, the bayesian model is selected as the sense disambiguation classifier and it is built based on discriminative features. The architecture of sense classification is given. The new algorithm is trained on sense-annotated corpus. Then it is used to determine its sense category. Experimental results show that the a

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45

Zhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.

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Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, know

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46

Komiya, Kanako, Minoru Sasaki, Hiroyuki Shinnou, and Manabu Okumura. "Domain Adaptation using Word Embeddings for Word Sense Disambiguation." Journal of Natural Language Processing 25, no. 4 (2018): 463–80. http://dx.doi.org/10.5715/jnlp.25.463.

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47

Hung, Jason C., and Che‐Yu Yang. "Word sense disambiguation using word ontology and concept distribution." Journal of the Chinese Institute of Engineers 32, no. 2 (2009): 153–68. http://dx.doi.org/10.1080/02533839.2009.9671494.

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48

Hatori, Jun, Yusuke Miyao, and Jun’ichi Tsujii. "On Contribution of Sense Dependencies to Word Sense Disambiguation." Journal of Natural Language Processing 16, no. 5 (2009): 51–77. http://dx.doi.org/10.5715/jnlp.16.5_51.

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49

Potapova, Rodmonga, Ksenia Oskina, and Vsevolod Potapov. "MODIFICATION OF TERM SENSE EMBEDDINGS REGARDING WORD-SENSE DISAMBIGUATION." Odessa linguistic journal 11 (2018): 53–57. http://dx.doi.org/10.32837/2312-3192-2018-11-53-57.

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50

Ruas, Terry, William Grosky, and Akiko Aizawa. "Multi-sense embeddings through a word sense disambiguation process." Expert Systems with Applications 136 (December 2019): 288–303. http://dx.doi.org/10.1016/j.eswa.2019.06.026.

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