Publication Type:

Conference Paper

Source:

CEUR Workshop Proceedings, CEUR-WS, Volume 1737, p.244-249 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006097416&partnerID=40&md5=3f39054f755d340d087a2da98f816821

Keywords:

Artificial intelligence, Classification (of information), Computational linguistics, Information Retrieval, Learning algorithms, Learning systems, Lexical database, NAtural language processing, Natural language processing systems, Paraphrase identifications, Part of speech tagging, Semantic concept, Semantic similarity, Semantics, Similarity computation, Weka

Abstract:

The paper reports the approaches utilized and results achieved for our system in the shared task (in FIRE-2016) for paraphrase identification in Indian languages (DPIL). Since Indian languages have a complex inherent nature, paraphrase identification in these languages becomes a challenging task. In the DPIL task, the challenge is to detect and identify whether a given sentence pairs paraphrased or not. In the proposed work, natural language processing with semantic concept extractions is explored for paraphrase detection in Hindi. Stop word removal, stemming and part of speech tagging are employed. Further similarity computations between the sentence pairs are done by extracting semantic concepts using WordNet lexical database. Initially, the proposed approach is evaluated over the given training sets using different machine learning classifiers. Then testing phase is used to predict the classes using the proposed features. The results are found to be promising, which shows the potency of natural language processing techniques and semantic concept extractions in detecting paraphrases.

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

K. Vani and Dr. Deepa Gupta, “ASE@DPIL-FIRE2016: Hindi paraphrase detection using natural language processing techniques & semantic similarity computations”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 244-249.

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