1. Noise in Text: Noise in text can come in the form of typos, slang, and other forms of incorrect or irrelevant text. This can make it difficult for natural language processing algorithms to accurately interpret the meaning of the text. For example, if a user types “I luv u” instead of “I love you”, an NLP algorithm might not be able to recognize the sentiment.
2. Ambiguity: Natural language is often ambiguous, making it difficult for NLP algorithms to accurately interpret the meaning of text. For example, the phrase “I saw her duck” can be interpreted in two different ways: either as a literal description of a duck being spotted, or as a figurative description of someone avoiding a situation.
3. Anaphora Resolution: Anaphora resolution is the task of determining the meaning of a pronoun or other word that refers back to a previously mentioned noun or phrase. For example, in the sentence “John ate the apple, and he was full”, the pronoun “he” refers back to “John”. An NLP algorithm needs to be able to recognize this reference in order to accurately interpret the meaning of the sentence.
4. Semantic Parsing: Semantic parsing is the task of extracting meaning from a sentence. For example, in the sentence “John is taller than Mary”, an NLP algorithm needs to be able to interpret the comparison between the two people and determine that John is taller than Mary.