{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:18:49Z","timestamp":1771651129667,"version":"3.50.1"},"reference-count":0,"publisher":"AGHU University of Science and Technology Press","issue":"1","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["csci"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.7494\/csci.2022.23.1.3977","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T21:08:08Z","timestamp":1648156088000},"source":"Crossref","is-referenced-by-count":9,"title":["Named-Entity Recognition for Hindi language using context pattern-based maximum entropy"],"prefix":"10.7494","volume":"23","author":[{"given":"Arti","family":"Jain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Divakar","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devendra Kr","family":"Tayal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuja","family":"Arora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4398","published-online":{"date-parts":[[2022,3,24]]},"container-title":["Computer Science"],"original-title":[],"link":[{"URL":"https:\/\/journals.agh.edu.pl\/csci\/article\/download\/3977\/2749","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.agh.edu.pl\/csci\/article\/download\/3977\/2749","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T21:08:09Z","timestamp":1648156089000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.agh.edu.pl\/csci\/article\/view\/3977"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,24]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3,24]]}},"URL":"https:\/\/doi.org\/10.7494\/csci.2022.23.1.3977","relation":{},"ISSN":["2300-7036","1508-2806"],"issn-type":[{"value":"2300-7036","type":"electronic"},{"value":"1508-2806","type":"print"}],"subject":[],"published":{"date-parts":[[2022,3,24]]}}}