Natural Language Processing (Part 45)-Part of Speech Tagging
📚Chapter 7: Part of Speech Tagging and Hidden Markov Models
Introduction
In this blog, you’re going to learn about parts of speech tagging. Specifically, you’ll see the different applications that it is used for. You’ll also learn how to compute the accuracy of a part-of-speech tagger. So, first up, I’ll explain what parts of speech tagging is all about. Then I’ll walk you through using Markov chains and hidden Markov models to assign parts of speech tags to your text. After that, I’ll dive into the Viterbi algorithm and show you how it comes into play with hidden Markov models.
Sections
What is part of Speech
Application of POS tagging
Section 1- What is part of Speech
Def: Parts of speech tagging, also known as POS tagging, is a fundamental task in Natural Language Processing (NLP). It involves assigning each word in a text a specific part of speech, such as noun, verb, adjective, or adverb, based on its definition and context. This process helps machines understand the grammatical structure and meaning of sentences, making it crucial for applications like machine translation, sentiment analysis, and information retrieval.
Def:Part of speech refers to the category of words or the lexical terms in a language. Examples of these lexical terms in the English language would be noun, verb, adjective, adverb, pronoun, preposition although there are many others. Let’s take a look at this sentence. Why not learn something? Writing out the names of these terms can quickly become cumbersome during text analysis.
You’re going to use a short representation called tags to represent these categories. The process of assigning these tags to the words of a sentence or your corpus is referred to as parts of speech tagging, or POS tagging for short.
Section 2- Application of POS tagging
Absolutely! Part-of-speech (POS) tags provide valuable information about the grammatical structure of a sentence. By analyzing these tags, you can infer the roles and relationships of words, which helps in understanding the underlying meaning or semantics of the text. For example, identifying nouns, verbs, and adjectives can give insights into the subjects, actions, and descriptions within a sentence. This can be particularly useful in natural language processing tasks like sentiment analysis, information extraction, and machine translation.
Since POS tags show how words are used in a sentence or text, you can use them to guess at what the words mean. They also help identify specific things like names of people or places.
In a sentence such as the Eiffel Tower is located in Paris, Eiffel Tower and Paris are both named entities.
Tags are also used for coreference resolution. If you have the two sentences, the Eiffel Tower is located in Paris, it is 324 meters high, you can use part-of-speech tagging to infer that it refers in this context to the Eiffel Tower.
Another application is speech recognition, where you use parts of speech tags to check if a sequence of words has a high probability or not. You now know the different applications of parts of speech tagging.
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To Do List
1- Collects Keys points from the blogs
Source
1- Natural Language Processing with Probabilistic Models (Coursera)
2-NERHMM: A TOOL FOR NAMED ENTITYR ECOGNITION BASED ON HIDDEN MARKOV MODEL
