Challenges during Natural Language Processing (NLP)
Natural Language Understanding (NLU)
This is the process of deciphering the intent of a word, phrase or sentence. This is a challenge in NLP that knowledge engineers might face, because when human beings speak, a natural language processor may not be able to understand that they, for instance, have an accent, they misused a word or phrase intentionally, they used their region’s variation of a language, they used filler words, they used initials, etc.
In Natural Language Processing (NLP) semantics, finding the meaning of a word is a challenge. A knowledge engineer may find it hard to solve the meaning of words have different meanings, depending on their use.
A knowledge engineer may face a challenge of trying to make an NLP extract the meaning of a sentence or message, captured through a speech recognition device even if the NLP has the meanings of all the words in the sentence. This challenge is brought about when humans state a sentence as a question, a command, a statement or if they complicate the sentence using unnecessary terminology.
Mutable Natural Language Structure
Natural languages can be mutated, that is, the same set of words can be used to formulate different meaning phrases and sentences. This poses a challenge to knowledge engineers as NLPs would need to have deep parsing mechanisms and very large grammar libraries of relevant expressions to improve precision and anomaly detection.
Natural Language Generation (NLG)
This allows computers to process natural language and respond to humans with natural language where necessary. This poses challenge to knowledge engineers because a computer would have to first process natural language, identify the intonation and sentence structure among many other things to respond accordingly if required, while following all language rules, which would have to all be hard coded or learnt by machine learning with human AI expert supervision.
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Challenges in Natural Language Processing (NLP)
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