Part of Speech (POS) Tagging

Exhibit 25.18   Part-of-speech tagging is the classification of words into their corresponding parts of speech.

Part-of-speech (POS) tagging is the process of classifying words in a text into their corresponding parts of speech and labeling them accordingly. Parts of speech are categories that describe the syntactic and grammatical roles of words within a sentence, such as nouns, verbs, adjectives, adverbs, and more. For example, as depicted in Exhibit 25.18, in the sentence “Family means nobody gets left behind or forgotten”, words like “family” and “nobody” would be tagged as nouns, while “means” would be tagged as a verb and “behind” as adverb.

Exhibit 25.19   Part-of-speech tag set.

The tag set refers to the collection of tags (Exhibit 25.19) used in a particular POS tagging task. These tags might be standard across different languages and tasks or custom-built for a specific application. Common tag sets include the Penn Treebank tag set, which is widely used in English language processing tasks.

The process of POS tagging involves using a part-of-speech tagger or POS-tagger, a tool that reads through a sequence of words (a sentence or a document) and assigns the appropriate part of speech to each word. For instance, NLTK’s nltk.pos_tag() function in Python is a commonly used POS-tagger. Given a list of words, this function attaches a POS tag to each word, allowing for deeper syntactic and semantic analysis of the text.

Overall, POS tagging is a crucial step in natural language processing (NLP) tasks such as parsing, information extraction, and machine translation, where understanding the grammatical structure of a sentence is essential.