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Learning to Detect Paraphrases in Indian Languages

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Text Processing (FIRE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10478))

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Abstract

In this paper, we present a system that detects paraphrases in Indian Languages- Hindi, Punjabi, Malayalam and Tamil. Our paraphrase detection method uses machine learning algorithms such as multinomial logistic regression and support vector machines trained with a variety of features which are basically various lexical and semantic level similarities between two sentences in a pair. With our developed paraphrase detection system, we participate in the shared Task on detecting paraphrases in Indian Languages (DPIL) organized by Forum for Information Retrieval Evaluation (FIRE) in 2016. This shared task consisted of two tasks-Task1 and Task2. We participated in task1 and task2 both for all four Indian Languages. We participate in the shared task with the system that uses multinomial logistic regression model and it was officially evaluated by the organizers of the contest against the test set released for the FIRE 2016 shared task on DPIL. After the conference, we enhance our system using another machine learning algorithm-Support Vector Machines and compare its performance with our previous systems. We present in this paper the description of our system, its performance in the shared task and its enhancement using Support Vector Machines. Our evaluation of the system based on the overall average system performance including task1 and task2 over all four languages reveals that the performance of our system is comparable to the best system participated in the shared task.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/download/confirmation.aspx?id=52398.

  2. 2.

    https://www.cs.york.ac.uk/semeval-2012/task6/index.html.

  3. 3.

    http://nlp.amrita.edu/dpil_cen/.

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Acknowledgments

This research work has received support from the project entitled ‘‘Design and Development of a System for Querying, Clustering and Summarization for Bengali’’ funded by the Department of Science and Technology, Government of India under the SERB scheme.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Kamal Sarkar

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  1. Kamal Sarkar
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Correspondence to Kamal Sarkar .

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Editors and Affiliations

  1. DAIICT, Gujarat, India

    Prasenjit Majumder

  2. Indian Statistical Institute, Kolkata, India

    Mandar Mitra

  3. DAIICT, Gujarat, India

    Parth Mehta

  4. DAIICT, Gujarat, India

    Jainisha Sankhavara

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Sarkar, K. (2018). Learning to Detect Paraphrases in Indian Languages. In: Majumder, P., Mitra, M., Mehta, P., Sankhavara, J. (eds) Text Processing. FIRE 2016. Lecture Notes in Computer Science(), vol 10478. Springer, Cham. https://doi.org/10.1007/978-3-319-73606-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-73606-8_12

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  • Print ISBN: 978-3-319-73605-1

  • Online ISBN: 978-3-319-73606-8

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