Rule based Part of speech Tagger for Homoeopathy Clinical realm

TL;DR

This paper introduces a rule-based part of speech tagger specifically designed for homoeopathy clinical language, utilizing pattern matching on clinical sentences to assign syntax classes with a focus on accuracy.

Contribution

It presents a simple, rule-based POS tagging approach tailored for homoeopathy clinical texts, addressing domain-specific language processing challenges.

Findings

Uses a corpus of 20,085 words with 125 sentences for evaluation

Achieves a focus on accuracy in tagging clinical language

Demonstrates the effectiveness of rule-based tagging in a specialized domain

Abstract

A tagger is a mandatory segment of most text scrutiny systems, as it consigned a s yntax class (e.g., noun, verb, adjective, and adverb) to every word in a sentence. In this paper, we present a simple part of speech tagger for homoeopathy clinical language. This paper reports about the anticipated part of speech tagger for homoeopathy clinical language. It exploit standard pattern for evaluating sentences, untagged clinical corpus of 20085 words is used, from which we had selected 125 sentences (2322 tokens). The problem of tagging in natural language processing is to find a way to tag every word in a text as a meticulous part of speech. The basic idea is to apply a set of rules on clinical sentences and on each word, Accuracy is the leading factor in evaluating any POS tagger so the accuracy of proposed tagger is also conversed.