[PDF] Top 20 Improving Word Sense Disambiguation Using Topic Features
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Improving Word Sense Disambiguation Using Topic Features
... The features used in these systems usually in- clude local features, such as part-of-speech (POS) of neighboring words, local collocations , syntac- tic patterns and global features such as single ... See full document
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Topic Models for Word Sense Disambiguation and Token Based Idiom Detection
... of topic models for sense ...pute topic models from a large unlabelled corpus and include them as features in a supervised sys- ...predominant word senses into a topic modelling ... See full document
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Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
... syntactic features identified in the previous sections. The probability of a sense to be the intended sense as identified by lexical and syntactic fea- tures is ...The sense which attains the ... See full document
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A System for Summarizing Scientific Topics Starting from Keywords
... the results of these techniques with the papers cov- ered by gold standard surveys on a few topics, we found that some important papers are missed by these simple approaches. One reason for this is that early papers in a ... See full document
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Enriching Wordnet for Word Sense Disambiguation
... linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word ...the word has ... See full document
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Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
... namely word sense er- rors, our approach differs in that we pair a human reference translation not just with one contrastive example, but a set of contrastive examples, ... See full document
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Improving Word Sense Disambiguation with Linguistic Knowledge from a Sense Annotated Treebank
... knowledge graph – whether the knowledge repre- sented in terms of nodes and relations (arcs) be- tween them is sufficient for the algorithm to pick the correct senses of ambiguous words. Several extensions of the ... See full document
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Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information
... the word sense for each word is given by the word sense selection model described in Section ...semantic features, e.g. semantic col- locations ( SEM-Col ) and word ... See full document
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Integrating Collocation Features in Chinese Word Sense Disambiguation
... the sense of ambiguous words in the fixed colloca- tions and strong collocations can be decided uniquely although they are not unique in loose ...ambiguous word “ 䴶Ⳃ ” in the collocation “ ጁᮄⱘ䴶Ⳃ ” may have ... See full document
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Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation
... for Word Sense Disambiguation ...of topic mod- els are available: (1) a topic model con- structed from the source domain corpus: (2) a topic model constructed from the tar- get ... See full document
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Word Sense Disambiguation using a Bidirectional LSTM
... for word sense ...to sense labels, and makes ef- fective use of word ...datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out ...specific ... See full document
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Word Sense Disambiguation using Static and Dynamic Sense Vectors
... one sense among the semantically ambiguous ones of the ...same sense (Rigau, et ...ambiguous word give more effective patterns or features than those far from it (Chen, et ...each sense ... See full document
7
Unsupervised, Knowledge Free, and Interpretable Word Sense Disambiguation
... frequent sense baselines, see Table 2. The latter picks the sense that cor- responds to the largest sense cluster (Panchenko et ...“per word” inventories, the model based on the con- text ... See full document
6
Measuring Topic Homogeneity and its Application to Dictionary Based Word Sense Disambiguation
... Measuring the semantic relatedness between words is an important area in NLP, and has been used in areas such as WSD, Lexical Chaining and Malapropism Detection. Budanitsky & Hirst (2006) evaluate 5 such measures, ... See full document
8
Word Sense Disambiguation vs Statistical Machine Translation
... employ features as described by Yarowsky and Florian (2002) in their “feature-enhanced naive Bayes model”, with position-sensitive, syntactic, and local collocational ... See full document
8
Word Sense Disambiguation with Multilingual Features
... with features drawn from multiple languages in order to generate a more robust and more effective vector-space representation that can be used for the task of word sense ...of features ... See full document
10
Simple Features for Chinese Word Sense Disambiguation
... topical features to local features that either included WordNet class features or used just lexical and named entity ...class features, but included topical keywords and passivization ... See full document
7
Semantic Based Document Clustering Using Lexical Chains
... semantic features extracted and exploits the characteristics of lexical chain based on ...performing word sense disambiguation to obtained candidate words based on the modified similarity ... See full document
7
Syntactic Features for High Precision Word Sense Disambiguation
... the sense that it only uses the positive information given by the first feature that holds in the test example (abstaining if none of them are ...By using a combination of the predictions of several ... See full document
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Improving Subcategorization Acquisition Using Word Sense Disambiguation
... [r] ... See full document
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