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In this paper, the Hindi NER task has been mapped into a multiclass learning problem, where the classes are NE tags. This paper presents a solution to this Hindi NER problem using a memory-based learning method. A set of simple and composite features, which includes binary, nominal, and string features, has been defined and incorporated into the proposed model. A relatively small Hindi Gazetteer list has also been employed to enhance the system performance. A comparative study on the experimental results obtained by the memory-based NER system proposed in this paper and a hidden Markov model (HMM)-based NER system shows that the performance of the proposed memory-based NER system is comparable to the HMM-based NER system.<\/jats:p>","DOI":"10.1515\/jisys-2015-0010","type":"journal-article","created":{"date-parts":[[2016,3,24]],"date-time":"2016-03-24T13:00:45Z","timestamp":1458824445000},"page":"301-321","source":"Crossref","is-referenced-by-count":6,"title":["A Memory-Based Learning Approach for Named Entity Recognition in Hindi"],"prefix":"10.1515","volume":"26","author":[{"given":"Kamal","family":"Sarkar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India"}]},{"given":"Sudhir Kumar","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India"}]}],"member":"374","published-online":{"date-parts":[[2016,3,24]]},"reference":[{"key":"2025120523263536021_j_jisys-2015-0010_ref_001_w2aab3b7c34b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"B. 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