1. Tokenization: This is the process of breaking down a sentence into smaller pieces, such as words or phrases. For example, “The cat sat on the mat” can be broken down into “The”, “cat”, “sat”, “on”, “the”, and “mat”.
2. Part-of-Speech (POS) Tagging: This is the process of assigning a part-of-speech label to each word in a sentence, such as noun, verb, adjective, adverb, etc. For example, “The cat sat on the mat” can be tagged as “Determiner (The) – Noun (cat) – Verb (sat) – Preposition (on) – Determiner (the) – Noun (mat)”.
3. Stemming and Lemmatization: This is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form. For example, “cats” can be reduced to “cat” and “running” can be reduced to “run”.
4. Named Entity Recognition (NER): This is the process of identifying and classifying named entities such as people, locations, organizations, and dates in a sentence. For example, “John works at Microsoft” can be recognized as “Person (John) – Organization (Microsoft)”.
5. Syntactic Parsing: This is the process of analyzing a sentence to determine its grammatical structure and relationship between the different components. For example, “John works at Microsoft” can be parsed as “Subject (John) – Verb (works) – Preposition (at) – Object (Microsoft)”.
6. Semantic Analysis: This is the process of analyzing the meaning of a sentence to determine its relationship with other sentences. For example, “John works at Microsoft” can be analyzed to determine that John is employed by Microsoft.