Word Sense Disambiguation (WSD) can be assisted by taking advantage of the metadata embedded in the various ontologies, lexica, databases, etc… that exist in the Semantic Web. Automated processes that exploit the links already present in the Semantic Web can strengthen parsing of word senses by using user-contributed and semantically-linked data. These processes are only possible because of a commitment to interoperability and the creation of shared standards. This paper will review some of the most heavily used Linguistic Linked Open Data (LLOD) tools and models which show the most promise for using metadata to alleviate problems caused by polysemous term
This book describes the state of the art in Word Sense Disambiguation. Current algorithms and applic...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and a...
Word Sense Disambiguation (WSD) can be assisted by taking advantage of the metadata embedded in the ...
Nowadays the textual information available online is provided in an increasingly wide range of lan-g...
Natural language is highly ambiguous, with the same word having different meanings depending on the ...
Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context....
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and a...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
BackgroundOntology term labels can be ambiguous and have multiple senses. While this is no problem f...
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in whi...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
This book describes the state of the art in Word Sense Disambiguation. Current algorithms and applic...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and a...
Word Sense Disambiguation (WSD) can be assisted by taking advantage of the metadata embedded in the ...
Nowadays the textual information available online is provided in an increasingly wide range of lan-g...
Natural language is highly ambiguous, with the same word having different meanings depending on the ...
Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context....
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and a...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known va...
BackgroundOntology term labels can be ambiguous and have multiple senses. While this is no problem f...
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in whi...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
This book describes the state of the art in Word Sense Disambiguation. Current algorithms and applic...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and a...