Mrinmaya Sachan

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Identifying Metaphorical Word Use with Tree Kernels

Dirk Hovy, Shashank Shrivastava, +5 authors Eduard H. Hovy
2013

A metaphor is a figure of speech that refers to one concept in terms of another, as in “He is such a sweet person”. Metaphors are ubiquitous and they present NLP with a range of challenges for WSD,… (More)

Using content and interactions for discovering communities in social networks

Mrinmaya Sachan, Danish Contractor, Tanveer A. Faruquie, L. Venkata Subramaniam
WWW
2012

In recent years, social networking sites have not only enabled people to connect with each other using social links but have also allowed them to share, communicate and interact over diverse… (More)

Learning Answer-Entailing Structures for Machine Comprehension

Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing, Matthew Richardson
ACL
2015

Understanding open-domain text is one of the primary challenges in NLP. Machine comprehension evaluates the system’s ability to understand text through a series of question-answering tasks on short… (More)

Spatial compactness meets topical consistency: jointly modeling links and content for community detection

Mrinmaya Sachan, Kumar Avinava Dubey, Shashank Srivastava, Eric P. Xing, Eduard H. Hovy
WSDM
2014

In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network. Assuming the social network to be an integer-weighted graph… (More)

Science Question Answering using Instructional Materials

Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing
ACL
2016

We provide a solution for elementary science tests using instructional materials. We posit that there is a hidden structure that explains the correctness of an answer given the question and… (More)

A Structured Distributional Semantic Model for Event Co-reference

Kartik Goyal, Sujay Kumar Jauhar, Huiying Li, Mrinmaya Sachan, Shashank Srivastava, Eduard H. Hovy
ACL
2013

In this paper we present a novel approach to modelling distributional semantics that represents meaning as distributions over relations in syntactic neighborhoods. We argue that our model… (More)

Machine Comprehension using Rich Semantic Representations

Machine comprehension tests the system’s ability to understand a piece of text through a reading comprehension task. For this task, we propose an approach using the Abstract Meaning Representation… (More)

Learning Concept Taxonomies from Multi-modal Data

Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing
ACL
2016

We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which… (More)

Self-Training for Jointly Learning to Ask and Answer Questions

Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the… (More)

Collective matrix factorization for co-clustering

We outline some matrix factorization approaches for co- clustering polyadic data (like publication data) using non-negative factorization (NMF). NMF approximates the data as a product of non-negative… (More)

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