Ambiguity in Natural Language Processing

Ambiguity is an intrinsic characteristic of human conversations and one that is particularly challenging in natural language understanding(NLU) scenarios by ambiguity, w are essentially referring to sentences that have multiple alternative interpretations.

Ambiguity is one of those areas of cognitive sciences that doesn’t have a well-defined solution. The spectrum of what can be considered ambiguous on any language varies greatly depending on the speaker. From a technical standpoint, any sentence in a language with a large-enough grammar can have alternative interpretations. However, most native speakers only recognize the primary interpretation when hearing a phrase while alternative representations may be more obvious to non-native speakers whom, cognitively speaking, need to rewire their brains in order to lean a new language. If humans find it difficult to deal with ambiguity in conversations, just imagine the challenge for NLU systems.

Types of Ambiguity

Technically defining ambiguity can, well, ambiguous. However, there are different forms of ambiguity that are relevant in natural language and, consequently, in artificial intelligence(AI) systems.

Lexical Ambiguity: This type of ambiguity represents words that can have multiple assertions. For instance, in English, the word “back” can be a noun ( back stage), an adjective (back door) or an adverb (back away).

Syntactic Ambiguity: This type of ambiguity represents sentences that can be parsed in multiple syntactical forms. Take the following sentence: “ I heard his cell phone rin in my office”. The propositional phrase “in my office” can e parsed in a way that modifies the noun or on another way that modifies the verb.

Semantic Ambiguity: This type of ambiguity is typically related to the interpretation of sentence. For instance, the previous sentence used in the previous point can be interpreted as if I was physically present in the office or as if the cell phone was in the office.

— Metonymy: Arguably, the most difficult type of ambiguity, metonymy deals with phrases in which the literal meaning is different from the figurative assertion. For instance, when we say “Samsung us screaming for new management”, we don’t really mean that the company is literally screaming (although you never know with Samsung these days ;) ).


Metaphors are a specific type of metonymy on which a phrase with one literal meaning is used as an analogy to suggest a different meaning. For example, if we say: “Roger Clemens was painting the corners”, we are not referring to the former NY Yankee star working as a painter.

Metaphors are particularly difficult to handle as they typically include references to historical or fictitious elements which are hard to place in the context of the conversation. From a conceptual standpoint, metaphors can be seen as a type of metonymy on which the relationship between sentences is based on similarity.

How to Handle Ambiguity?

In AI theory, The process of handling ambiguity is called disambiguation . Those techniques are very challenging to handle with the current generation of AI technologies. I would cover more on that topic on a future post.

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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