Publication Type:

Conference Paper

Source:

CEUR Workshop Proceedings, CEUR-WS, Volume 1737, p.309-312 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006153431&partnerID=40&md5=033afebb3e2c80e72c997ef94a44471b

Keywords:

Artificial intelligence, Character recognition, Conditional random field, Data mining, Entity extractions, Entity recognition, Hybrid features, Indian languages, Information Retrieval, Modeling languages, random processes, Sequential model, Social media, Training corpus

Abstract:

Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plethora of information is available in social media in the form of text and due to its nature of free style representation, it introduces much complexity while mining information out of it. This complexity is enhanced more by representing the text in more than one language and the usage of transliterated words. In this work we utilized sequential modeling algorithm with hybrid features to perform the Entity Recognition on the corpus given by CMEE-IL (Code Mixed Entity Extraction - Indian Language) organizers. The experimented approach performed great on both the Tamil-English and Hindi-English tweet corpus by attaining nearly 95% against the training corpus and 45.17%, 31.44% against the testing corpus.

Notes:

cited By 0; Conference of 2016 Forum for Information Retrieval Evaluation, FIRE 2016 ; Conference Date: 7 December 2016 Through 10 December 2016; Conference Code:125007

Cite this Research Publication

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Conditional random fields for code mixed Entity Recognition”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 309-312.

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