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Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TLDR
It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. Expand
Effective multi-label active learning for text classification
TLDR
This work proposes a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy, and demonstrates that this approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods. Expand
End-to-End Learning for Structured Prediction Energy Networks
TLDR
End-to-end learning for SPENs is presented, where the energy function is discriminatively trained by back-propagating through gradient-based prediction, and the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016). Expand
Joint Inference for Fine-grained Opinion Extraction
TLDR
Experimental results demonstrate that the joint inference approach significantly outperforms traditional pipeline methods and baselines that tackle subtasks in isolation for the problem of opinion extraction. Expand
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
TLDR
A novel hierarchical distance-dependent Bayesian model for event coreference resolution that allows for the incorporation of pairwise distances between event mentions to guide the generative clustering processing for better event clustering both within and across documents. Expand
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
TLDR
KBLSTM is proposed, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading and achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset. Expand
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
TLDR
A tensor decomposition approach for knowledge base embedding that is highly scalable, and is especially suitable for relation extraction by leveraging relational domain knowledge about entity type information, which is significantly faster than previous approaches and better able to discover new relations missing from the database. Expand
Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization
TLDR
A novel context-aware method for analyzing sentiment at the level of individual sentences that encoding intuitive lexical and discourse knowledge as expressive constraints and integrating them into the learning of conditional random field models via posterior regularization is proposed. Expand
Joint Extraction of Events and Entities within a Document Context
TLDR
This paper proposes a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document to enable access to document-level contextual information and facilitate context-aware predictions. Expand
A Joint Sequential and Relational Model for Frame-Semantic Parsing
TLDR
This work introduces a new method for frame-semantic parsing that significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction. Expand
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