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        <title>IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification</title>
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    <name type="personal">
        <namePart type="given">Pruthwik</namePart>
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    <name type="personal">
        <namePart type="given">Prathyusha</namePart>
        <namePart type="family">Danda</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Soujanya</namePart>
        <namePart type="family">Lanka</namePart>
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            <title>Proceedings of the IJCNLP 2017, Shared Tasks</title>
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            <namePart type="given">Chao-Hong</namePart>
            <namePart type="family">Liu</namePart>
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            <namePart type="family">Nakov</namePart>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
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    <abstract>The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.</abstract>
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%0 Conference Proceedings
%T IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification
%A Mishra, Pruthwik
%A Danda, Prathyusha
%A Kanneganti, Silpa
%A Lanka, Soujanya
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F mishra-etal-2017-iiit
%X The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.
%U https://aclanthology.org/I17-4008
%P 53-58

Markdown (Informal)

[IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification](https://aclanthology.org/I17-4008) (Mishra et al., IJCNLP 2017)

• IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification (Mishra et al., IJCNLP 2017)

ACL

• Pruthwik Mishra, Prathyusha Danda, Silpa Kanneganti, and Soujanya Lanka. 2017. IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 53–58, Taipei, Taiwan. Asian Federation of Natural Language Processing.