We'll do the absolute basics for each and compare the results. Let's start with some simple examples of POS tagging with three common Python libraries: NLTK 4, TextBlob 5, and Spacy 6. These are not always considered POS but are often included in POS tagging libraries. Determiner or Article: A grammatical marker of definiteness (the) or indefiniteness (a, an).Interjection: An interjection is a word used to express emotion.Conjunction: A conjunction joins words, phrases, or clauses.Preposition: A preposition is a word placed before a noun or pronoun to form a phrase modifying another word in the sentence.Adverb: An adverb modifies or describes a verb, an adjective, or another adverb.Adjective: An adjective modifies or describes a noun or pronoun.Verb: A verb expresses action or being. Pronoun: A pronoun is a word used in place of a noun.Noun: A noun is the name of a person, place, thing, or idea.There are eight (sometimes nine 1) different parts of speech in English that are commonly defined 3. These tags, in turn, can be used as features for higher-level tasks such as building parse trees, which can, in turn, be used for Named Entity Resolution, Coreference Resolution, Sentiment Analysis, and Question Answering 2. POS tagging builds on top of that, and phrase chunking builds on top of POS tags. At the bottom are sentence and word segmentation. There is a hierarchy of tasks in NLP (see Natural language processing for a list). While POS tags are used in higher-level functions of NLP, it's important to understand them on their own, and it's possible to leverage them for useful purposes in your text analysis. In Natural Language Processing (NLP), POS is an essential building block of language models and interpreting text. Part of Speech (POS) is a way to describe the grammatical function of a word 1.
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