Sunday, 22 March 2020

Lexical ambiguity in natural language processing

Lexical ambiguity in natural language processing, Definition of lexical ambiguity, types of ambiguity, examples of lexical ambiguity, how to resolve lexical ambiguity, what is lexical ambiguity

Lexical ambiguity 

It is class of ambiguity caused by a word and its multiple senses especially when the word is part of sentence or phrase. A word can have multiple meanings under different part of speech categories. Also, under each POS category they may have multiple different senses. Lexical ambiguity is about choosing which sense of a particular word under a particular POS category.
In a sentence, the lexical ambiguity is caused while choosing the right sense of a word under a correct POS category. 
For example, let us take the sentence “I saw a ship”. Here, the words “saw” and “ship” would mean multiple things as follows;
Saw = present tense of the verb saw (cut with a saw) OR past tense of the verb see (perceive by sight) OR a noun saw (blade for cutting) etc. According to WordNet, the word “saw” is defined under 3 different senses in NOUN category and under 25 different senses in VERB category.
Ship = present tense of the verb ship (transport commercially) OR present tense of the verb ship (travel by ship) OR a noun ship (a vessel that carries passengers) etc. As per WordNet, the word “ship” is defined with 1 sense under NOUN category and 5 senses under VERB category.
Due to multiple meanings, there arises an ambiguity in choosing the right sense of “saw” and “ship”.
Handling lexical ambiguity
Lexical ambiguity can be handled using the tasks like POS tagging and Word Sense Disambiguation.


What is lexical ambiguity?

Define lexical ambiguity in NLP

Why lexical ambiguity is one of  the problems in NLP

Types of ambiguity

Example English sentences with lexical ambiguity 

how to handle lexical ambiguity

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