Word sense representation is important in the tasks of information retrieval(IR).Existing lexical databases,e.g.,WordNet,and automated word sense representing approaches often use only one view to represent a word,and may not work well in the tasks which are sensitive to the contexts,e.g.,query rewriting.In this paper,we propose a new framework to represent a word sense simultaneously in two views,explanation view and context view.We further propose an novel method to automatically learn such representations from large scale of query logs.Experimental results show that our new sense representations can better handle word substitutions in a query rewriting task.1-1
Word sense induction (WSI) seeks to automat-ically discover the senses of a word in a cor-pus via un...
Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identi...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
Word sense representation is important in the tasks of information retrieval (IR). Existing lexical ...
Information retrieval using word senses is emerging as a good research challenge on semantic informa...
Most word representation methods assume that each word owns a single semantic vec-tor. This is usual...
This paper proposes an Information Retrieval (IR) system that integrates sense discrimination to ove...
This paper proposes an algorithm for word sense disambiguation based on a vector representation of w...
Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural ...
The problems of word sense disambiguation and document indexing for information retrieval have been ...
Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is in...
In this paper we exploit Semantic Vectors to develop an IR system. The idea is to use semantic space...
Contextualized word embeddings have been employed effectively across several tasks in Natural Langua...
The representation of written language semantics is a central problem of language technology and a c...
Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is i...
Word sense induction (WSI) seeks to automat-ically discover the senses of a word in a cor-pus via un...
Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identi...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
Word sense representation is important in the tasks of information retrieval (IR). Existing lexical ...
Information retrieval using word senses is emerging as a good research challenge on semantic informa...
Most word representation methods assume that each word owns a single semantic vec-tor. This is usual...
This paper proposes an Information Retrieval (IR) system that integrates sense discrimination to ove...
This paper proposes an algorithm for word sense disambiguation based on a vector representation of w...
Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural ...
The problems of word sense disambiguation and document indexing for information retrieval have been ...
Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is in...
In this paper we exploit Semantic Vectors to develop an IR system. The idea is to use semantic space...
Contextualized word embeddings have been employed effectively across several tasks in Natural Langua...
The representation of written language semantics is a central problem of language technology and a c...
Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is i...
Word sense induction (WSI) seeks to automat-ically discover the senses of a word in a cor-pus via un...
Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identi...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....