Dependency Position Encoding for Relation Extraction
Leveraging the dependency tree of the input sentence is able to improve the model performance for relation extraction. A challenging issue is how to remove confusions from the tree. Efforts have been made to utilize the dependency connections between words to selectively emphasize target-relevant information. However, these approaches are limited in focusing on exploiting dependency types. In this paper, we propose dependency position encoding (DPE), an efficient way of incorporating both dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies for the task. In contrast to previous studies that process input sentence and dependency information in separate streams, DPE can be seamlessly incorporated into the Transformer and makes it possible to use an one-stream scheme to extract relations between entity pairs. Extensive experiments show that models with our DPE significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED.
Code (0)
등록된 구현이 없습니다.
Tasks
PositionRelationRelation ExtractionSentenceSimilar Papers 제목 키워드 기반
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based…
Aspect Term Extraction and Sentiment ClassificationPOSPositionTerm ExtractionDocument-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization
One key challenge in multi-document summarization is to capture the relations among input documents that distinguish between single document summarization (SDS) and multi-document summarization (MDS). Few existing MDS wo…
Document SummarizationMulti-Document SummarizationAn Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency Structure
Aspect-based sentiment analysis has gained significant attention in recent years due to its ability to provide fine-grained insights for sentiment expressions related to specific features of entities. An important compon…
Aspect-Based Sentiment AnalysisAspect ExtractionDependency ParsingSentiment AnalysisA Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification
Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences
Relation extraction as an important natural Language processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to ca…
PositionRelationRelation ExtractionRelation Prediction+1