All types of ambiguities in NLP

Since Natural language can be open to multiple interpretations at times, this would pass on to the computers who will try to understand the natural language input given to them. Often, it can be difficult to fully understand a sentence when we are not given enough context or if there is poor grammar.

In this article we will be going over many different types of ambiguities that are found in NLP.

Part Of Speech (POS) Tagging Ambiguity

POS tagging refers to the process of classifying words in a text to a part of speech - whether the word is a verb, noun, etc. Often, you will find that the same word can take on multiple classifications for its part of speech depending on how the sentence is constructed. For example, it is quite common to see words that can be used both as a verb or a noun −

• I need to mail my friend the files. (Verb)

• I need to find the mail that was sent to me. (Noun)

Structural Ambiguity

This ambiguity arises because the same exact sentence can be interpreted differently based on how the sentence is parsed. Take the following sentence −

The boy kicked the ball in his jeans. 

This sentence can be construed as the boy either kicking the ball while wearing his jeans, or kicking the ball while the ball was in the jeans. This depends on how the sentence is parsed.

Scope Ambiguity

Here we look at ambiguities that occur due to quantifiers. Taking a look back at some math logic terminology, or just basic grammar, we know that words like ‘every’ and ‘any’ would come to mind.

Take the following sentence −

All students learn a programming language.

This sentence, due to the scope created with the sequential use of quantifiers ‘all’ followed by ‘a’, can have two different meanings. The two meanings are that −

• The first is that all students learn the same programming language.

• They all learn a language that doesn’t have to be the same one.

Lexical Ambiguity

Certain words have the property that they can have multiple different meanings. There are two forms of lexical ambiguity that exist: Polysemy and Homonymy.

Polysemy − This is when two words are the same but have a different meaning depending on the usage, i.e the word Foot. Foot can describe the body part, or the foot of the building. Essentially, you are describing the base of something with the word foot.

Homonym − This occurs when a word has the same spelling or pronunciation, but has different meanings overall. While superficially the same they are completely different in meaning. The word bass for example can be referring to the musical instrument, or a type of fish. Another example, which is given here to clarify that not just spelling but pronunciation is important too, is horse and hoarse. These two have similar pronunciations, but horse refers to the animal and hoarse refers to a sore throat.

Semantic Ambiguity

Now, instead of a word having multiple meanings, sentences can have multiple meanings depending on the context. For example, the sentence “He ate the burnt lasagna and pie” could mean one of two things −

• That the lasagna was burnt and the pie wasn’t.

• That both were burnt.

Lexical ambiguity can be deemed a subtype of semantic ambiguity.

Referential Ambiguity

Referential ambiguity occurs when a phrase can have multiple interpretations due to the use of multiple objects and the referencing not being clear. For example, take the sentence −

I looked at Michelle with the telescope.

This can mean two things depending on who has the telescope.

• Michelle herself was carrying the telescope.

• The person saying the sentence was using a telescope to see Michelle.

Anaphoric Ambiguity

Here we have a loosely similar ambiguity to referential ambiguity, but more fixated on pronouns . The use of pronouns can cause some confusion if there are multiple people being mentioned in a sentence. Take the following sentence −

Michelle told Romany that she ate the cake.

Now, from the sentence alone it is not exactly clear whether ‘she’ is referring to Michelle or Romany.

Conclusion

Here we looked deep into linguistics and in particular ambiguities. Given that Natural Language Processing deals with natural language (English specifically for the most part), we honed our skills in linguistics in this lesson, and this will serve to help in processing various natural language input and create algorithms to understand what is being said.