Python Natural Language Processing
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Lexical ambiguity

Lexical ambiguity is word-level ambiguity. A single word can have ambiguous meaning in terms of its internal structure and its syntactic class. Let's look at some examples:

  • Sentence 1: Look at the stars. Here, look is a verb.
  • Sentence 2: The person gave him a warm look. Here, look is a noun.
  • Sentence 3: She won three silver medals. Here, silver is a noun.
  • Sentence 4: She made silver speech. Here, silver is a adjective.
  • Sentence 5: His stress had silvered his hair. Here, silvered is a verb.

In the preceding examples, specific words change their POS tags according to their usage in sentence structure. This kind of ambiguity can been resolved by using two approaches:

  • By using accurate POS tagger tools, this kind of ambiguity can be resolved
  • WordNet sense has various scenes available for a word when the words take specific POS tag. This also helps to handle ambiguity

Many Indian languages have the same issue of lexical ambiguity.