[PDF] Top 20 Learning Semantic Classes for Word Sense Disambiguation
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Learning Semantic Classes for Word Sense Disambiguation
... Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition ...for learning coarser and more gen- eral set of concepts from a sense tagged corpus, in order ... See full document
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SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical Semantic Combinations
... Evaluation benchmarks and measures We used five test sets standardized with WordNet 3.0 (Raganato et al., 2017a) including the English all- words tasks from Senseval-2 (Edmonds and Cot- ton, 2001), Senseval-3 (Snyder and ... See full document
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An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation
... For a couple of the verbs examined, we found that active learning gives higher prediction accuracy midway through the training than one gets after training on the entire corpus. Analysis suggests that this is not ... See full document
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A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
... This paper describes a set of comparative exper- iments, including cross-corpus evaluation, be- tween five alternative algorithms for supervised Word Sense Disambiguation WSD, namely Nai[r] ... See full document
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Subjectivity Word Sense Disambiguation
... of sense inventories for WSD. They aim for a more coarse- grained sense inventory to overcome performance shortcomings related to fine-grained sense distinc- ...the sense that we reduce all ... See full document
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A Review on Word Sense Disambiguation
... vague word corresponding to an entire sentence of situation in determining power ...a word, and then chose the heading whose contained words were most prominent in the ...considered sense frequencies ... See full document
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An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model
... two sense definitions (glosses) to select the proper meaning in a ...possible word meaning, and 2) the information about the context in which the word ...on word co-occurrences in ...each ... See full document
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Neural Sequence Learning Models for Word Sense Disambiguation
... Several recent contributions (Søgaard and Gold- berg, 2016; Bjerva et al., 2016; Plank et al., 2016; Luong et al., 2016) have shown the effectiveness of multitask learning (Caruana, 1997, MTL) in a sequence ... See full document
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Word Sense Disambiguation by Learning from Unlabeled Data
... In a series of experiments on word sense disambiguation of Korean nouns we observed that the accuracy is improved up to 20.2% using only 32% of labeled data.. This implies, the learning [r] ... See full document
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Domain Adaptation with Active Learning for Word Sense Disambiguation
... predominant sense, or most frequent sense (MFS), of each noun in our WSJ test data perfectly, and we assign this most frequent sense to each noun in the test data, we will have achieved an accuracy ... See full document
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WORD SENSE DISAMBIGUATION USING FUZZY SEMANTIC-BASED STRING SIMILARITY MODEL
... Sentences are the language of human communication. This communication medium is so fluid that words and meaning can have many interpretations by readers. Besides, a document that consists of thousands of sentences would ... See full document
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Learning a Stopping Criterion for Active Learning for Word Sense Disambiguation and Text Classification
... active learning process end when repeated cycles show no significant performance improvement on the test ...active learning process can end if there is no significant performance improvement on the ... See full document
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Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
... tivity Word Sense Disambiguation (SWSD), a coarse-grained word sense disambiguation ...a learning effect associated with repeatedly completing the same kind of ... See full document
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Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks
... knowledge base used to define word senses and provide a basis for their disambiguation. How- ever, very good results are obtained only when a rich and large knowledge base is utilised. In such a case, ... See full document
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Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
... with semantic features constantly ben- efit from such ...new word types, or expand the number of senses covered by the already existing classifiers (Fig ... See full document
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Latent Semantic Word Sense Induction and Disambiguation
... induced sense inventory for the unsuper- vised disambiguation of particular occurrences of ...the disambiguation step are based on the same principle: words and contexts are mapped to a limited ... See full document
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Learning Expressive Models for Word Sense Disambiguation
... supervised learning (see Mihalcea et ...machine learning algorithms and attribute-value vectors to represent disambigu- ation ...the disambiguation models, that is, knowledge that goes beyond simple ... See full document
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PageRank on Semantic Networks, with Application to Word Sense Disambiguation
... competing word senses which tend to share targets of incoming or outgoing ...same word – that is, if they share one identical lexical ...given word or word phrase, colex- ical synsets will be ... See full document
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Experiments on Active Learning for Croatian Word Sense Disambiguation
... fort, rendering such approaches particularly cost- ineffective for smaller languages. On the other hand, supervised approaches do not rely on lexi- cal resources and generally outperform knowledge- based approaches ... See full document
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