Background Knowledge in Learning-Based Relation Extraction

2012 ◽  
Author(s):  
Quang X. Do
Keyword(s):  
Relation Extraction ◽  
Background Knowledge

scholarly journals Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Guimin Huang ◽  
Jianheng Chen ◽  
Yabing Wang
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Background Knowledge ◽  
Word Embedding ◽  
Entity Recognition ◽  
Instance Selection ◽  
Attention Model ◽  
Training Tool ◽  
Knowledge Based ◽  
Proposed Model

Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.

scholarly journals Injecting Logical Background Knowledge into Embeddings for Relation Extraction

2015 ◽  
Author(s):  
Tim Rocktäschel ◽  
Sameer Singh ◽  
Sebastian Riedel
Keyword(s):  
Relation Extraction ◽  
Background Knowledge

Feature distribution and background knowledge in category learning

2000 ◽  
Vol 53 (4) ◽  
pp. 962-982 ◽  
Author(s):  
Gregory L. Murphy ◽  
Audrey S. Kaplan
Keyword(s):  
Category Learning ◽  
Background Knowledge

Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

2018 ◽  
Author(s):  
Prachi Jain ◽  
Shikhar Murty ◽  
Mausam . ◽  
Soumen Chakrabarti
Keyword(s):  
Knowledge Base ◽  
Hybrid Model ◽  
Matrix Factorization ◽  
Relation Extraction ◽  
Tensor Factorization ◽  
Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

SCHNAPPER: A Web Toolkit for Exploratory Relation Extraction

2015 ◽  
Author(s):  
Thilo Michael ◽  
Alan Akbik
Keyword(s):  
Relation Extraction

Distant Supervision for Relation Extraction with Matrix Completion

2014 ◽  
Author(s):  
Miao Fan ◽  
Deli Zhao ◽  
Qiang Zhou ◽  
Zhiyuan Liu ◽  
Thomas Fang Zheng ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Relation Extraction ◽  
Distant Supervision
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