Sunday, March 22, 2020

Ambiguity in Natural Language Processing

What is ambiguity in natural language processing? Types of ambiguity in NLP. Which step of NLP is ambiguous? Define ambiguity in NLP. Examples of ambiguity with explanation.


Ambiguity in computational linguistics is a situation where a word or a sentence may have more than one meaning. That is, a sentence may be interpreted in more than one way. This leads to uncertainty in choosing the right meaning of a sentence especially while processing natural languages by computer.

  • Ambiguity is a challenging task in natural language understanding (NLU).
  • The process of handling the ambiguity is called as disambiguation.
  • Ambiguity presents in almost all the steps of natural language processing. (Steps of NLP – lexical analysis, syntactic analysis, semantic analysis, discourse analysis, and pragmatic analysis).

Consider the following sentence for an example;
Raj tried to reach his friend on the mobile, but he didn’t attend”
In this sentence, we have the presence of lexical, syntactic, and anaphoric ambiguities.

  • Lexical ambiguity – The word “tried” means “attempted” not “judged” or “tested”. Also, the word “reach” means “establish communication” not “gain” or “pass” or “strive”.

  • Syntactic ambiguity – The phrase “on the mobile” attached to “reach” and thus means “using the mobile”. It is not attached to “friend”.

  • Anaphoric ambiguity – The anaphor “he” refers the “friend” not “Raj”.

The following are the types of ambiguity with respect to natural language processing task;


What are the challenges in ambiguity resolution in NLP?

What is ambiguity?

Define ambiguity in NLP

Why ambiguity is the biggest problem in NLP

Types of ambiguity

Example English sentences with ambiguity

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