Computational linguistics constantly faces the issue of ambiguity due to the fact that words and sentences are usually ambiguous, and computers possess no sense of context ...
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Computational linguistics constantly faces the issue of ambiguity due to the fact that words and sentences are usually ambiguous, and computers possess no sense of context like humans and cannot address it easily. As another example, the word bank can be applied to a riverbank as well as a bank in the financial sense and a sentence constructed such as I saw the man with the telescope can be misinterpreted.
In order to clear up ambiguity, NLP systems commonly apply contextual clues, statistical models and gigantic language models (LLMs). To reduce possible meanings, techniques such as use of part-of-speech tagging, word sense disambiguation and syntactic parsing are employed. The current powerful LLM model such as BERT or GPT learns the context through neighbouring words reducing its dependency on preceding language contexts compared to the previous rule-based models. Nevertheless, the comprehension is still rather difficult and particularly when the context is insinuated or a culturally-specific meaning is intended.
