[PDF] Top 20 Word Sense Clustering and Clusterability
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Word Sense Clustering and Clusterability
... of clusterability of instances can predict the partitionability of a ...of clusterability measures in the best possible case, we want the representations of the instances that we cluster to be as ... See full document
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Clustering and Diversifying Web Search Results with Graph Based Word Sense Induction
... result clustering outperforms all other approaches across all evaluation measures on the two corpora, except for KeySRC and the singleton baseline when using the F1 ... See full document
46
A Semi Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
... when combined with most of dimensionality reduc- tion techniques. This result confirmed our previous conclusion that using unlabeled data can improve the sense disambiguation process. Furthermore, SemiFC performs ... See full document
8
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance
... a sense clustering could be trivially induced from the map- ...the clustering would be well-founded, as the ODE sense groupings were manually crafted by expert ...structing sense ... See full document
8
WoSIT: A Word Sense Induction Toolkit for Search Result Clustering and Diversification
... It has been estimated that around 4% of Web queries and 16% of the most frequent queries are ambiguous (Sanderson, 2008). A major issue as- sociated with the lexical ambiguity phenomenon on the Web is the low number of ... See full document
6
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 04)
... Chinese Verb Sense Discrimination Using an EM Clustering Model with Rich Linguistic Features Jinying Chen and Martha Palmer.. Relieving the data Acquisition Bottleneck in Word Sense Disa[r] ... See full document
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Making Sense of Word Embeddings
... a sense inventory from raw cor- pora. Such unsupervised sense induction meth- ods fall into two categories: context clustering, such as (Pedersen and Bruce, 1997; Sch¨utze, 1998; Reisinger and ... See full document
10
Measuring the Impact of Sense Similarity on Word Sense Induction
... Spectral Clustering Spectral Clustering inter- prets a dataset’s elements as vertices in graph with edges based on their similarity (Ng et ...tral clustering to WSI, which was performed on a Chinese ... See full document
11
Evaluating Unsupervised Ensembles when applied to Word Sense Induction
... induced sense labels generated for a set of test contexts with human annotated sense using a fixed sense ...a clustering solution and measures accuracy with simple metrics such as the Paired ... See full document
6
Gamification for Word Sense Labeling
... All disambiguation models rely on gold standard data from human annotators, but this data is time- consuming and expensive to obtain. In the context of constructing the Groningen Meaning Bank (GMB, Basile et al., 2012), ... See full document
6
A Review on Word Sense Disambiguation
... Knowledge-based methods rely on information that can be extracted or inferred from a knowledge source, such as a dictionary, thesaurus or lexical database. These methods learn based on information from curetted and ... See full document
6
Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering
... erate word embeddings. These word embeddings are then used by a Sentence Composition Model, which takes glosses in WordNet as positive train- ing data and randomly replaces part of the sen- tences as ... See full document
6
Fine Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets
... Out of the 211 set of targeted words, with 1644 occurrences the system could not make a decision for 38 (18 %) words with 63 occurrences (3.83%). Most of these words were happax legomena (21) for which neither the ... See full document
7
Chinese Word Sense Induction with Basic Clustering Algorithms
... Word Sense Induction (WSI) or Word Sense Discrimination is a task of automatically discov- ering word senses from un-annotated ...from Word Sense Disambiguation (WSD) ... See full document
5
Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation
... the clustering purity, (Davidson et ...decrease clustering pu- ...hierarchical clustering algorithm fol- lows in a unconstrained fashion until some moder- ate number of clusters are ... See full document
10
Chinese Word Sense Induction based on Hierarchical Clustering Algorithm
... identify word senses of polysemous words encountered in a ...Unsupervised word sense induction can be viewed as a clustering ...Hierarchical Clustering Algorithm as the classifier for ... See full document
5
Word Sense Ambiguation: Clustering Related Senses
... WORD SENSE AMBIGUATION CLUSTERING RELATED SENSES W O R D S E N S E A M B I G U A T I O N C L U S T E R I N G R E L A T E D S E N S E S William B Dolan Microsoft Research billdol @ microsoft corn A b s[.] ... See full document
5
Clustering Paraphrases by Word Sense
... input word or ...for clustering paraphrases by word sense, and apply it to the Paraphrase Database ...spectral clustering algorithms, and systematically explore different ways of ... See full document
10
Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces
... of word sense discrimination techniques using first order and sec- ond order context vectors, where both can be employed in similarity and vector ...UPGMA clustering algorithm are the most effec- ... See full document
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