Description
Title: Using context pattern-based maximum entropy, NAMED-ENTITY RECOGNITION FOR HINDI LANGUAGE
Abstract: This paper describes a named-entity-recognition (NER) system for the Hindi language that employs two methodologies: the proposed context pattern-based MENE (CP-MENE) framework and the baseline maximum entropy-based named-entity (BL-MENE) model. Although BL-MENE makes use of a number of baseline features for the NER task, it has issues with named-entity (NE) boundary detection that is inaccurate, classification errors, and the partial recognition of NEs as a result of some essentials being absent. The CP-MENE-based NER task, however, includes numerous features and patterns that are designed to get around these issues. Right-boundary, left-boundary, part-of-speech, synonym, gazetteer, and relative pronoun features are actually among the features of CP-MENE. For the purpose of extracting highly ranked NE patterns generated by regular expressions using Python code, CP-MENE develops a kind of recursive relationship. This work is conducted on the Hindi health data (HHD) corpus because there is an increase in Hindi-language web content today (especially in applications related to health care) (which is readily available from the Kaggle dataset). Four NE categories—Person (PER), Disease (DIS), Consumable (CNS), and Symptom—were used in our experiments (SMP).
Keywords: context patterns, gazetteer lists, Hindi language, Kaggle dataset, maximum entropy, named-entity recognition, feature extension
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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