Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
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TLDR
In this paper, a multichannel recurrent neural network with long short-term memory (LSTM) units was proposed to classify the relation of two entities in a sentence.Abstract:
Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an F1-score of 83.7%, higher than competing methods in the literature.read more
Citations
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Proceedings ArticleDOI
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
TL;DR: The experimental results on the SemEval-2010 relation classification task show that the AttBLSTM method outperforms most of the existing methods, with only word vectors.
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End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Makoto Miwa,Mohit Bansal +1 more
TL;DR: A novel end-to-end neural model to extract entities and relations between them and compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8).
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
TL;DR: A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.
Proceedings ArticleDOI
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.
TL;DR: An extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel is proposed, and a novel pruning strategy is applied to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold.
Proceedings ArticleDOI
Position-aware Attention and Supervised Data Improve Slot Filling
TL;DR: An effective new model is proposed, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction that builds TACRED, a large supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations.
References
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