Add to ORKGThis article describes the results of a systematic in-depth study of the criteria used for word sense disambiguation. Our study is based on 60 target words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line with some practices in the field. For example, we show that omitting non-content words decreases performance and that bigrams yield better results than unigrams
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of d...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
This book describes the state of the art in Word Sense Disambiguation. Current algorithms and applic...
We identified features that drive differential accuracy in word sense disambiguation (WSD) by buildi...
Tesis presentada en diciembre de 2004 por la Universidad del País Vasco bajo la supervisión del Dr....
The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task...
Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
Current approaches to word sense disambiguation use (and often combine) various machine learning tec...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Word sense disambiguation automatically determines the appropriate senses of a word in context. We h...
The need for robust and easily extensible systems for word sense disambiguation coupled with succe...
This task was meant to compare the results of two different retrieval techniques: the first one was ...
The main research question I try to answer in the my thesis is which linguistic knowledge sources ar...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of d...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
This book describes the state of the art in Word Sense Disambiguation. Current algorithms and applic...
We identified features that drive differential accuracy in word sense disambiguation (WSD) by buildi...
Tesis presentada en diciembre de 2004 por la Universidad del País Vasco bajo la supervisión del Dr....
The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task...
Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core...
Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is d...
Current approaches to word sense disambiguation use (and often combine) various machine learning tec...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Word sense disambiguation automatically determines the appropriate senses of a word in context. We h...
The need for robust and easily extensible systems for word sense disambiguation coupled with succe...
This task was meant to compare the results of two different retrieval techniques: the first one was ...
The main research question I try to answer in the my thesis is which linguistic knowledge sources ar...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of d...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...