NATURAL LANGUAGE PROCESSING

INTRODUCTION

One of the fundamental Artificial Intelligence problems prevalent today is the inability of a computer program to be able to convert a piece of English text into a programmer friendly

data structure that describes the meaning of the natural language text. As long as no consensus has emerged about the form or the existence of such a data structure, scientists must settle for the lesser objective of extracting simpler representations that describe limited aspects of the textual information.

These simpler representations are often motivated by specific applications or by our belief that they capture something more general about natural language. They can describe syntactic information (e.g., part-of-speech tagging, chunking, and parsing) or semantic information (e.g., word-sense disambiguation, semantic role labeling, named entity extraction, and anaphora resolution). Text corpora have been manually annotated with such data structures in order to compare the performance of various systems. The availability of standard benchmarks has stimulated research in Natural Language Processing (NLP) and effective systems have been designed for all these tasks

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APA

Ogunwale, F. (2018). NATURAL LANGUAGE PROCESSING. Afribary. Retrieved from https://tracking.afribary.com/works/natural-language-processing-1177

MLA 8th

Ogunwale, Funbi "NATURAL LANGUAGE PROCESSING" Afribary. Afribary, 29 Jan. 2018, https://tracking.afribary.com/works/natural-language-processing-1177. Accessed 14 Jan. 2026.

MLA7

Ogunwale, Funbi . "NATURAL LANGUAGE PROCESSING". Afribary, Afribary, 29 Jan. 2018. Web. 14 Jan. 2026. < https://tracking.afribary.com/works/natural-language-processing-1177 >.

Chicago

Ogunwale, Funbi . "NATURAL LANGUAGE PROCESSING" Afribary (2018). Accessed January 14, 2026. https://tracking.afribary.com/works/natural-language-processing-1177

NATURAL LANGUAGE PROCESSING

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