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Advanced Machine Learning Techniques in Natural Language Processing for Indian Languages

  • Vaishali GuptaEmail author
  • Nisheeth Joshi
  • Iti Mathur
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

The paper represents the advanced NLP learning resources in context of Indian languages: Hindi and Urdu. The research is based on domain-specific platforms which covers health, tourism, and agriculture corpora with 60 k sentences. With these corpora, some NLP-based learning resources such as stemmer, lemmatizer, POS tagger, and MWE identifier have been developed. All of these resources are connected in sequential form, and they are beneficial in information retrieval, language translation, handling word sense disambiguation, and many other useful applications. Stemming is first and foremost process of root extraction from given input word, but sometimes it does not produce valid root word. So the problem of stemming has been resolved by developing Lemmatizer, which produces the exact root by adding some rules in stemmed output. Eventually, statistical POS tagger has been designed with the help of Indian Government (TDIL) tagset (Indian Govt. Tagset, [1]). With this POS-tagged file, MWE identifier was developed. However, for developing MWE identifier, some rules are created for MWE tagset and then MWE-tagged file has been developed which in turn produces the automatic extraction of the MWEs from tagged corpora using CRF\({+}{+}\) tool. Moreover, evaluation of learning resources has been performed to calculate the accuracy, and as a result, the output of corresponding proposed resources such as stemmer, lemmatizer, POS tagger, and MWE identifier are 77.0, 86.8, 73.20, and 43.50% for Hindi and 74.0, 85.4, 84.97 and 47.2% for Urdu, respectively.

Keywords

Stem Lemma POS Tags Tagset Hindi Urdu and MWE 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Banasthali VidyapithRajasthanIndia

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