A Manual on Context Analysis in Natural Language Processing

A Manual on Context Analysis in Natural Language Processing


Millions of tweets are processed every day by data analysts to understand the concept of “how people feel”. But, the utilization of context analysis in data analytics services tells why people feel that way. A practical natural language processing in an AI system must be good in making disambiguation decisions of word sense, word category, syntactic structure, and semantic scope. A Statistical NLP strategy seeks to solve these problems by learning lexical and structural preferences automatically from corpora.

Concepts of NLP Applications In Business

People’s expressions need to be read and understood well where stemming and lemmatization works with root forms of multiple variations. Those variations include Fuzzy matching, Phrase matching and Paraphrase detections. This involves operations like string or word representations. The number of such operations is determined based upon the extent of coverage requirement in a step versus performance cost.
SpaCy is an open-source library for NLP in Python supporting operations like tokenization, dependency parsing or noun phrase extraction. These Tokenization, and POS tagging help in identifying the concepts in content.
Read More: https://www.mitosistech.com/a-manual-on-context-analysis-in-natural-language-processing/

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