SEE: Syntax-aware Entity Embedding for Neural Relation Extraction

01/11/2018
by   Zhengqiu He, et al.
0

Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve state-of-the-art performance of relation extraction.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/17/2019

BERE: An accurate distantly supervised biomedical entity relation extraction network

Automated entity relation extraction (RE) from literature provides an im...
research
10/11/2018

Neural Relation Extraction Within and Across Sentence Boundaries

Past work in relation extraction mostly focuses on binary relation betwe...
research
08/21/2018

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Extracting relations is critical for knowledge base completion and const...
research
09/23/2020

A Comparative Study on Structural and Semantic Properties of Sentence Embeddings

Sentence embeddings encode natural language sentences as low-dimensional...
research
02/04/2023

FGSI: Distant Supervision for Relation Extraction method based on Fine-Grained Semantic Information

The main purpose of relation extraction is to extract the semantic relat...
research
06/04/2021

Entity Concept-enhanced Few-shot Relation Extraction

Few-shot relation extraction (FSRE) is of great importance in long-tail ...
research
04/17/2020

Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction

Despite the recent progress, little is known about the features captured...

Please sign up or login with your details

Forgot password? Click here to reset