Abstract

Part-of-speech (POS) tagging is an indispensable method of text processing. The main aim is to assign part-of-speech to words after considering their actual contextual syntactic-cum-semantic roles in a piece of text where they occur (Siemund & Claridge 1997). This is a useful strategy in language processing, language technology, machine learning, machine translation, and computational linguistics as it generates a kind of output that enables a system to work with natural language texts with greater accuracy and success. Part-of-speech tagging is also known as ‘grammatical annotation’ and ‘word category disambiguation’ in some area of linguistics where analysis of form and function of words are important avenues for better comprehension and application of texts. Since the primary task of POS tagging involves a process of assigning a tag to each word, manually or automatically, in a piece of natural language text, it has to pay adequate attention to the contexts where words are used. This is a tough challenge for a system as it normally fails to know how word carries specific linguistic information in a text and what kind of larger syntactic frames it requires for its operation. The present paper takes up this issue into consideration and tries to critically explore how some of the well-known POS tagging systems are capable of handling this kind of challenge and if these POS tagging systems are at all successful in assigning appropriate POS tags to words without accessing information from extratextual domains. The novelty of the paper lies in its attempt for looking into some of the POS tagging schemes proposed so far to see if the systems are actually successful in dealing with the complexities involved in tagging words in texts. It also checks if the performance of these systems is better than manual POS tagging and verifies if information and insights gathered from such enterprises are at all useful for enhancing our understanding about identity and function of words used in texts. All these are addressed in this paper with reference to some of the POS taggers available to us. Moreover, the paper tries to see how a POS tagged text is useful in various applications thereby creating a sense of awareness about multifunctionality of tagged texts among language users.

Highlights

  • An electronically developed corpus, after it is annotated at the part-of-speech level, becomes useful for various works of language analysis, processing, application and reference in language technology, applied linguistics, translation, dictionary compilation, language teaching and description (Sinclair 2004)

  • The main aim is to assign part-of-speech to words after considering their actual contextual syntactic-cumsemantic roles in a piece of text where they occur (Siemund & Claridge 1997). This is a useful strategy in language processing, language technology, machine learning, machine translation, and computational linguistics as it generates a kind of output that enables a system to work with natural language texts with greater accuracy and success

  • In descriptive and applied linguistics, for instance, POS tagging of words is necessary because we find that words are able to represent different parts-of-speech in different contexts

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Summary

Introduction

An electronically developed corpus (i.e., digital language database), after it is annotated at the part-of-speech level, becomes useful for various works of language analysis, processing, application and reference in language technology, applied linguistics, translation, dictionary compilation, language teaching and description (Sinclair 2004). Since the primary task of POS tagging involves a process of assigning a tag to each word, manually or automatically, in a piece of natural language text, it has to pay adequate attention to the contexts where words are used.

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