Named-Entity Recognition for Hindi language using Context Pattern-Based Maximum Entropy

Arti Jain; Divakar Yadav; Anuja Arora; Devendra K. Tayal

doi:10.7494/csci.2022.23.1.3977

Authors

Arti Jain Jaypee Institute of Information Technology, Noida, UP, India
Divakar Yadav NIT Hamirpur Himachal Pradesh, India
Anuja Arora
Devendra K. Tayal

DOI:

https://doi.org/10.7494/csci.2022.23.1.3977

Keywords:

Context Patterns, Gazetteer Lists, Hindi Language, Kaggle Datasets, Maximum Entropy, Named Entity Recognition, Feature Extension

Abstract

This paper describes Named Entity Recognition (NER) system for Hindi language using two methodologies. An existing BaseLine Maximum Entropy-based Named Entity (BL-MENE) model and Context Pattern-based MENE (CP-MENE) framework the one proposed in this work. BL-MENE utilizes several features for the NER task but suffers from inaccurate Named Entity (NE) boundary detection, mis-classification errors, and partial recognition of NEs due to certain missing essentials. However, CP-MENE based NER task incorporates extensive features and patterns set to overcome these problems. In fact, the CP-MENE features include right-boundary, left-boundary, part-of-speech, synonyms, gazetteers and relative pronoun features. CP-MENE formulates a kind of recursive relationship to extract high ranked NE patterns that are generated through regular expressions via python@ code. Nowadays, since the Web contents in the Hindi language are rising, especially in the health-care applications, this work is conducted on the Hindi Health Data (HHD) corpus at Kaggle dataset. We conducted experiments on four NE categories- Person (PER), Disease (DIS), Consumable (CNS) and Symptom (SMP). Usually, researchers’ work upon PER NE within news articles while other NEs, especially related to the health-care domain such as DIS, CNS, and SMP NE types are left out which are incorporated in this research. CP-MENE improvised the classification performance of NEs and the F-measure achieved are 79.68% for PER, 72.50% for DIS, 68.78% for CNS, and 67.23% for SMP respectively which are comparable with respect to other NER approaches.

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Author Biographies

Arti Jain, Jaypee Institute of Information Technology, Noida, UP, India

Department of Computer Science & Engineering

Assistant Professor (Sr Grade)

Jaypee Institute of Information Technology, Noida, UP, India

Divakar Yadav, NIT Hamirpur Himachal Pradesh, India

Department of Computer Science & Engineering

Associate Professor

NIT Hamirpur, India

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