Word sense disambiguation

WORD SENSE DISAMBIGUATION

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University Introduction

In language understanding applications , word sense disambiguation (WSD is a technique used to distinguish the meaning of a word in a sentence WSD is a field in the extensive body of natural language processing (NLP , which is responsible for relevant tasks such as speech segmentation , syntactic ambiguity , among numerous others . Both WSD and NLP are applied in computational linguistics , in modeling natural languages

Contents - Origins

WSD models

Applications of WSD

Problems in WSD

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Origins

WSD was first actualized in the early 20th century , after computers proved effective in solving arithmetic tasks . WSD was not widely known until 1949 when weaver introduced it on machine translation

It was researchers such as Masterman , in 1957 , who popularized WSD in representing the different senses of a word . Almost a decade later Mandhu and Lytle calculated sense frequencies of words in different domains and also applied Bayes formula to choose the most probable sense given a context

In the later years WSD faced numerous challenges , but was rejuvenated in the 1990s , when word net became available and Senseval began . Senseval for instance , made it possible to compare and evaluate different systems in test words , annotators , sense inventories and corpora

The recent use of parallel bilingual corporal is an example of the new research measures being implemented , after the 2004 plea to implement them

WSD Models

There are two commonly used WSD models : the symbolic and probabilistic models . The symbolic models...