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Results 1 - 10
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175
Unsupervised Domain Tuning to Improve Word Sense Disambiguation
"... The topic of a document can prove to be useful information for Word Sense Disambiguation (WSD) since certain meanings tend to be associated with particular topics. This paper presents an LDA-based approach for WSD, which is trained using any available WSD system to establish a sense per (Latent Diri ..."
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The topic of a document can prove to be useful information for Word Sense Disambiguation (WSD) since certain meanings tend to be associated with particular topics. This paper presents an LDA-based approach for WSD, which is trained using any available WSD system to establish a sense per (Latent
Unsupervised domain relevance estimation for word sense disambiguation
- In Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP’04
, 2004
"... This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain category. We use a pre-defined set of categories (we call them domains) which have been previously associ ..."
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Cited by 3 (0 self)
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that maximize the likelihood of the model on the empirical data. The correct identification of the domain of the text is a crucial point for Domain Driven Disambiguation, an unsupervised Word Sense Disambiguation (WSD) methodology that makes use of only domain information. Therefore, DRE has been exploited
A Topic Model for Word Sense Disambiguation
, 2007
"... We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider each ..."
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Cited by 59 (5 self)
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We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider
Word Sense Disambiguation in Clinical Text
, 2013
"... Lexical ambiguity, the ambiguity arising from a string with multiple meanings, is pervasive in lan-guage 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 ..."
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Lexical ambiguity, the ambiguity arising from a string with multiple meanings, is pervasive in lan-guage 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
Word Domain Disambiguation via Word Sense Disambiguation
"... Word subject domains have been widely used to improve the performance of word sense disambiguation algorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject domains. The few existing approaches have focused on the development of algorithms specifi ..."
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Word subject domains have been widely used to improve the performance of word sense disambiguation algorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject domains. The few existing approaches have focused on the development of algorithms
Domain kernels for word sense disambiguation
- In Proceedings of the 43 rd annual meeting of the Association for Computational Linguistics (ACL-05
, 2005
"... In this paper we present a supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions. In particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a kernel function, namely the ..."
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Cited by 25 (8 self)
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In this paper we present a supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions. In particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a kernel function, namely
Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
"... In this paper we investigate the challenges of applying statistical machine translation to meeting conversations, with a particular view towards analyzing the importance of modeling contextual factors such as the larger discourse context and topic/domain information on translation performance. We de ..."
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Cited by 2 (1 self)
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statistical machine translation systems. Finally, we demonstrate how the largest source of translation errors (lack of topic/domain knowledge) can be addressed by applying documentlevel, unsupervised word sense disambiguation, resulting in performance improvements over the baseline system. 1
Unsupervised Word Sense Disambiguation with Multilingual Representations
"... In this paper we investigate the role of multilingual features in improving word sense disambiguation. In particular, we explore the use of semantic clues derived from context translation to enrich the intended sense and therefore reduce ambiguity. Our experiments demonstrate up to 26 % increase in ..."
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Cited by 1 (0 self)
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In this paper we investigate the role of multilingual features in improving word sense disambiguation. In particular, we explore the use of semantic clues derived from context translation to enrich the intended sense and therefore reduce ambiguity. Our experiments demonstrate up to 26 % increase
Unsupervised Learning of Word Sense Disambiguation Rules By Estimating an
- in EM Algorithm, IPSJ Journal, Vol.44, No.12, 2003(in Japanese
"... In this paper, we improve an unsupervised learning method using the ExpectationMaximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation (WSD) problems. The improved method stops the EM algorithm at the optimum iteration n ..."
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In this paper, we improve an unsupervised learning method using the ExpectationMaximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation (WSD) problems. The improved method stops the EM algorithm at the optimum iteration
Results 1 - 10
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