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Books on the topic 'Multilingual Word Sense Disambiguation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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3

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

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4

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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5

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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6

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

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7

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

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8

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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9

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

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10

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

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11

Stevenson, Mark, and Yorick Wilks. Word-Sense Disambiguation. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0013.

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Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage. The main approaches to tackle the problem were dictionary-based, connectionist, and statistical strategies. This article concludes with a review of evaluation strategies for WSD and possible applications of the technology. WSD is an ‘intermediate’ task in language processing: like part-of-speech tagging or syntactic anal

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12

Eneko, Agirre, and Edmonds Philip Glenny, eds. Word sense disambiguation: Algorithms and applications. Springer, 2007.

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13

(Editor), Eneko Agirre, and Philip Edmonds (Editor), eds. Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology). Springer, 2006.

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14

(Editor), Eneko Agirre, and Philip Edmonds (Editor), eds. Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology). Springer, 2007.

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15

Hristea, Florentina T. T. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection. Springer, 2012.

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16

Hristea, Florentina T. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection. Springer, 2012.

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17

Stevenson, Mark. Word Sense Disambiguation (Center for the Study of Language and Information - Lecture Notes). Center for the Study of Language and Inf, 2002.

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18

Stevenson, Mark. Word Sense Disambiguation (Center for the Study of Language and Information - Lecture Notes). Center for the Study of Language and Inf, 2002.

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19

Chen, Jinying. Towards High-Performance Word Sense Disambiguation- Combining Rich Linguistic Knowledge and Machine Learning Approaches. VDM Verlag Dr. Mueller e.K., 2007.

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20

New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation Springerbriefs in Electrical and Computer Engineering. Springer, 2012.

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21

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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Abstract:

This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b

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