Word Sense Disambiguation (WSD)

By Asma Kausar and Joshim Uddin

Introduction to WSD● In computation linguistics is the process of

identifying and analyzing the meaning of words in context.

● An open problem of Natural Language Processing.

● Governs the process of which 'sense' of a word is used, since one word written or pronounced has different meanings

ProblemsTake the following examples:● “ The rebel seized the opportunity to rebel”

Rebel has two meanings, same spelling but pronounced differently

First used being a Noun which indicates the 'person' who resists authority, control or convention and the latter being a Verb being the action taken by the person.

Problems● “I read a book and it had a red cover”

Same Pronunciation but spelt differently with different meanings

● To a human, it is common sense what it is● Developing Algorithms to replicate this human ability can be a

difficult task, as is further exemplification by implicit equivocation between “read” (Best book I've read) and “read” (I read the newspaper)

When relating to Machine Translation it is identified as a distinctive task.

One of the first problems faced by the systems was word sense ambiguity, it became apparent the semantic dis ambiguity at the lexical level

Early Approaches To WSD● Three earlier attempts to solve WSD● Preference Semantic: In simple words a representation of entire

sentence would be build up from the representation of the individual word through the process of semantic interpretation.

● Word Expert Prasing: This approach is more highly lexicalized.This approach is based on assumptions of human knowledge about knowledge of words rather knowledge about rules.

● Polaried words: A system which is less lexicalized. This system cantain modules same in the NLP system like grammer, prasers, lexicon and sematic interpretor.

● Issues with all three: Lack of lexical hierarchy, the high degree of lexicalization.

Approaches To WSD● Dictionary Based Approach – By Michiel E Lesk● First Machine readable dictionary method● Use Lesk Algorithm – based on assumption that words in a

given "neighborhood" will tend to share a common subject.● For example algorithm used for 'pine' 'cone'.● 'Pine' has two major senses in Oxford dictionary, 'tree with

needle shaped leaves' and 'Waste away through sorrow or illness'.

● Whereas 'cone' has three meaning,'solid body which narrows to a point','something of this shape whether solid or hollo' and 'fruit of certain evergreen trees'.

Machine Learning Approaches to WSD

● Tagging with Thesaurus catagories:-developed by mastermman in 1960

-- Used simple algorithm that based on repitition of catagories accociated with words in the same sentence. Approach remained unsuccessful but later was improved by using statical medal of catagories

-- The later method was successful when tested on 12 Polysemy word (as 92 correct disambigaution word were found)

Machine Learning Approaches to WSD

● Clustering word Usages -● Uses Yarowsky algorithm for unsupervised learning to

find hidden structure in unlabeled data. Uses one sense per collocation and one sense per discourse properties of human language

● Decision list is based on ‘One sense per collocation’ property and start with large set of possible collocations and calculate log-likelihood ratio of word-sense probability for each collocation. The Higher log-likelihood the more predictive evidence.

Summary● WSD is a long standing problem in Language processing● Early problems suffered from lack of coverage due to lack

of lexical resources

● The earlier approaches were not fully successful as they had some issues like Lack of lexical hierarchy, the high degree of lexicalization, due to large size of vocabulary.

● Most Successful was the Machine Learning approaches