Manajit Chakraborty

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In information retrieval, keyword-based queries often fail to capture actual information need, especially when the need is very specific and particular. Using natural language, however, a user can clearly tell what she wants (positive part) and what she does not (negative parts). We propose techniques for automatic removal of negative parts and query(More)
The TREC 2016 Contextual Suggestion task aims at providing recommendations on points of attraction for different kind of users and a varying context. Our group DPLAB IITBHU tries to recommend relevant point-of-interests to a user based on the information provided on the candidate attractions and her past preferences. We employ open-web information in a(More)
In this paper, we present our approach for the Contextual Suggestion track of 2015 Text REtrieval Conference (TREC). The task aims at providing recommendations on points of attraction for different kind of users and a varying context. Our group DPLAB IITBHU tries to address the problem from the perspective of how relevant the attractions are based on user(More)
Contextual Suggestion deals with search techniques for complex information needs that are highly focused on context and user needs. In this paper, we propose R-Rec, a novel rule-based technique to identify and recommend appropriate points-of-interest to a user given her past preferences. We try to embody the information that the user shares in the form of(More)
Sentiment drifts due to people changing their opinions instantly on microblogs e.g. Twitter, are a major challenge in sentiment analysis. In this paper, we have developed a method that selects most frequent messages from a relevant message set constructed using state-of-the-art sampling approaches. Our proposed technique increases the robustness of the(More)
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