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20142026
most citedLeveraging Knowledge Bases in LSTMs for Improving Machine Reading

28 citations · 106 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CL2019

Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

Igor Labutov, Bishan Yang, Anusha Prakash +1

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shape…

cs.CL2019★ 28 cited

Leveraging Knowledge Bases in LSTMs for Improving Machine Reading

Bishan Yang, Tom Mitchell

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge f…

cs.CL2019

Learning to Learn Semantic Parsers from Natural Language Supervision

Igor Labutov, Bishan Yang, Tom Mitchell

As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency.…

cs.CL2016★ 22 cited

Joint Extraction of Events and Entities within a Document Context

Bishan Yang, Tom Mitchell

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is…

cs.CL2015★ 8 cited

A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution

Bishan Yang, Claire Cardie, Peter Frazier

We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely…

cs.CL2014

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Bishan Yang, Wen-tau Yih, Xiaodong He +2

We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) an…

cs.CL2014★ 21 cited

Learning Multi-Relational Semantics Using Neural-Embedding Models

Bishan Yang, Wen-tau Yih, Xiaodong He +2

In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational…