Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task
International audienceIn this article, we tackle the issue of the limited quantity of manually sense...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
WORDNET makes a great number of fine-grained word sense distinctions. However, what could be seen as...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
The granularity of word senses in current general purpose sense inventories is often too �ne-grained...
We present a method for clustering word senses of a lexical-semantic resource by mapping them to tho...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses,...
Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation [K...
Current Word Sense Disambiguation systems show an extremely poor performance on low fre- quent sense...
Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meaning...
The paper presents a method for word sense disambiguation based on parallel corpora. The method expl...
International audienceIn this article, we tackle the issue of the limited quantity of manually sense...
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
International audienceIn this article, we tackle the issue of the limited quantity of manually sense...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
WORDNET makes a great number of fine-grained word sense distinctions. However, what could be seen as...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
The granularity of word senses in current general purpose sense inventories is often too �ne-grained...
We present a method for clustering word senses of a lexical-semantic resource by mapping them to tho...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses,...
Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation [K...
Current Word Sense Disambiguation systems show an extremely poor performance on low fre- quent sense...
Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meaning...
The paper presents a method for word sense disambiguation based on parallel corpora. The method expl...
International audienceIn this article, we tackle the issue of the limited quantity of manually sense...
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
International audienceIn this article, we tackle the issue of the limited quantity of manually sense...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
WORDNET makes a great number of fine-grained word sense distinctions. However, what could be seen as...