Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of word sense disambiguation. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, independent, methods and we include the design of such a tagger. A prototype of this system has been implemented, correctly tagging 88% of polysemous word tokens in a small test set, providing evidence that our hypothesis is correct. 1 Sense Tagging There has been a tendency in word sense disambiguation (WSD hereafter) literature to carry out different disambi...
This paper presents and evaluates models created according to a schema that provides a description o...
Most word representation methods assume that each word owns a single semantic vec-tor. This is usual...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
There has been a tradition of combining different knowledge sources in Artificial Intelligence resea...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradi...
There has been a tradition of combining differ-ent knowledge sources in Artificial Intelligence rese...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depe...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- sem...
Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context....
This paper presents and evaluates models created according to a schema that provides a description o...
This paper presents and evaluates models created according to a schema that provides a description o...
Most word representation methods assume that each word owns a single semantic vec-tor. This is usual...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
There has been a tradition of combining different knowledge sources in Artificial Intelligence resea...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradi...
There has been a tradition of combining differ-ent knowledge sources in Artificial Intelligence rese...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depe...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- sem...
Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context....
This paper presents and evaluates models created according to a schema that provides a description o...
This paper presents and evaluates models created according to a schema that provides a description o...
Most word representation methods assume that each word owns a single semantic vec-tor. This is usual...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...