Easy First Relation Extraction with Information Redundancy

Many existing relation extraction (RE) models make decisions globally using integer linear programming (ILP). However, it is nontrivial to make use of integer linear programming as a blackbox solver for RE. Its cost of time and memory may become unacceptable with the increase of data scale, and redundant information needs to be encoded cautiously for ILP. In this paper, we propose an easy first approach for relation extraction with information redundancies, embedded in the results produced by local sentence level extractors, during which conflict decisions are resolved with domain and uniqueness constraints. Information redundancies are leveraged to support both easy first collective inference for easy decisions in the first stage and ILP for hard decisions in a subsequent stage. Experimental study shows that our approach improves the efficiency and accuracy of RE, and outperforms both ILP and neural network-based methods.

[1]  Dongyan Zhao,et al.  Joint Inference for Knowledge Base Population , 2014, EMNLP.

[2]  Andrew McCallum,et al.  Joint inference of entities, relations, and coreference , 2013, AKBC '13.

[3]  Doug Downey,et al.  A Probabilistic Model of Redundancy in Information Extraction , 2005, IJCAI.

[4]  L. Getoor,et al.  1 Global Inference for Entity and Relation Identification via a Linear Programming Formulation , 2007 .

[5]  Xiang Li,et al.  Joint inference for cross-document information extraction , 2011, CIKM '11.

[6]  Luke S. Zettlemoyer,et al.  Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations , 2011, ACL.

[7]  D. Roth.  1 Global Inference for Entity and Relation Identification via a Linear Programming Formulation , 2007 .

[8]  Oren Etzioni,et al.  Identifying Relations for Open Information Extraction , 2011, EMNLP.

[9]  Mirella Lapata,et al.  Unsupervised Relation Extraction with General Domain Knowledge , 2013, JSSP.

[10]  Oren Etzioni,et al.  Open Information Extraction from the Web , 2007, CACM.

[11]  Zhiyuan Liu,et al.  Neural Relation Extraction with Selective Attention over Instances , 2016, ACL.

[12]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[13]  Claire Cardie,et al.  Joint Extraction of Entities and Relations for Opinion Recognition , 2006, EMNLP.

[14]  Barbara Plank,et al.  Using Frame Semantics for Knowledge Extraction from Twitter , 2015, AAAI.

[15]  Dan Roth,et al.  A Linear Programming Formulation for Global Inference in Natural Language Tasks , 2004, CoNLL.

[16]  Razvan C. Bunescu,et al.  Learning to Extract Relations from the Web using Minimal Supervision , 2007, ACL.

[17]  Serge Abiteboul,et al.  Foundations of Databases , 1994 .

[18]  Ramesh Nallapati,et al.  Multi-instance Multi-label Learning for Relation Extraction , 2012, EMNLP.

[19]  Heng Ji,et al.  Incremental Joint Extraction of Entity Mentions and Relations , 2014, ACL.

[20]  Jun Zhao,et al.  Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks , 2015, EMNLP.

[21]  Gerhard Weikum,et al.  J-REED: Joint Relation Extraction and Entity Disambiguation , 2017, CIKM.

[22]  Andrew McCallum,et al.  Collective Cross-Document Relation Extraction Without Labelled Data , 2010, EMNLP.

[23]  Wenguang Chen,et al.  Extending Conditional Dependencies with Built-in Predicates , 2015, IEEE Transactions on Knowledge and Data Engineering.

[24]  Valentin I. Spitkovsky,et al.  A Simple Distant Supervision Approach for the TAC-KBP Slot Filling Task , 2010, TAC.

[25]  Shuai Ma,et al.  Extending dependencies for improving data quality , 2011 .

[26]  Daniel S. Weld,et al.  Type-Aware Distantly Supervised Relation Extraction with Linked Arguments , 2014, EMNLP.

[27]  Veselin Stoyanov,et al.  Easy-first Coreference Resolution , 2012, COLING.

[28]  Dongyan Zhao,et al.  Encoding Relation Requirements for Relation Extraction via Joint Inference , 2014, ACL.

[29]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[30]  Daniel S. Weld,et al.  Ontological Smoothing for Relation Extraction with Minimal Supervision , 2012, AAAI.

[31]  Heng Ji,et al.  Joint Event Extraction via Structured Prediction with Global Features , 2013, ACL.

[32]  Jens Lehmann,et al.  DBpedia - A crystallization point for the Web of Data , 2009, J. Web Semant..

[33]  Jun Zhao,et al.  Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions , 2017, AAAI.

[34]  Rohit J. Kate,et al.  Joint Entity and Relation Extraction Using Card-Pyramid Parsing , 2010, CoNLL.

[35]  Xifeng Yan,et al.  Global Relation Embedding for Relation Extraction , 2018, NAACL-HLT.

[36]  Wenfei Fan,et al.  Foundations of Data Quality Management , 2012, Foundations of Data Quality Management.

[37]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[38]  Li Guo,et al.  Knowledge Base Completion Using Embeddings and Rules , 2015, IJCAI.