Difference between revisions of "Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia"
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| − | '''Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Zhaohui Wu and C. Lee Giles. | + | '''Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Zhaohui Wu]] and [[C. Lee Giles]]. |
== Overview == | == Overview == | ||
| − | Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. Authors present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The "sense-specific" prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness. | + | Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. Authors present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on [[Wikipedia]], which could account for homonymy and polysemy. The "sense-specific" prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic [[relatedness]] for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness. |
Revision as of 07:34, 1 July 2019
Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia - scientific work related to Wikipedia quality published in 2015, written by Zhaohui Wu and C. Lee Giles.
Overview
Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. Authors present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The "sense-specific" prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.
