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Found 16 papers, 2 papers with code
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge.
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading.
Learning to Learn Semantic Parsers from Natural Language Supervision
As humans, we often rely on language to learn language.
A Joint Sequential and Relational Model for Frame-Semantic Parsing
We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art.
End-to-End Learning for Structured Prediction Energy Networks
Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016).
Joint Extraction of Events and Entities within a Document Context
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon.
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
We consider learning representations of entities and relations in KBs using the neural-embedding approach.
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Learning Multi-Relational Semantics Using Neural-Embedding Models
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 embedding models under the framework.
Joint Modeling of Opinion Expression Extraction and Attribute Classification
In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification.
