End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories

In this paper, we extend distant supervision (DS) based on Wikipedia for Relation Extraction (RE) by considering (i) relations defined in external repositories, e.g. YAGO, and (ii) any subset of Wikipedia documents. We show that training data constituted by sentences containing pairs of named entities in target relations is enough to produce reliable supervision. Our experiments with state-of-the-art relation extraction models, trained on the above data, show a meaningful F1 of 74.29% on a manually annotated test set: this highly improves the state-of-art in RE using DS. Additionally, our end-to-end experiments demonstrated that our extractors can be applied to any general text document.

[1]  Jian Su,et al.  A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features , 2006, ACL.

[2]  Luis Gravano,et al.  Snowball: extracting relations from large plain-text collections , 2000, DL '00.

[3]  Ralph Grishman,et al.  Discovering Relations among Named Entities from Large Corpora , 2004, ACL.

[4]  Jian Su,et al.  Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering , 2005, IJCNLP.

[5]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[6]  Alessandro Moschitti,et al.  Kernel methods, syntax and semantics for relational text categorization , 2008, CIKM '08.

[7]  Mark A. Przybocki,et al.  The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation , 2004, LREC.

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

[9]  Daniel S. Weld,et al.  Learning 5000 Relational Extractors , 2010, ACL.

[10]  Nanda Kambhatla,et al.  Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction , 2004, ACL.

[11]  Andrew McCallum,et al.  Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.

[12]  Razvan C. Bunescu,et al.  A Shortest Path Dependency Kernel for Relation Extraction , 2005, HLT.

[13]  Dmitry Zelenko,et al.  Kernel Methods for Relation Extraction , 2002, J. Mach. Learn. Res..

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

[15]  Alessandro Moschitti,et al.  Kernel-based Reranking for Named-Entity Extraction , 2010, COLING.

[16]  Michael Collins,et al.  Convolution Kernels for Natural Language , 2001, NIPS.

[17]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[18]  Alexander Yates.  Extracting World Knowledge from the Web , 2009, Computer.

[19]  Alessandro Moschitti,et al.  Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction , 2009, EMNLP.

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

[21]  Alessandro Moschitti,et al.  A Study on Convolution Kernels for Shallow Statistic Parsing , 2004, ACL.

[22]  Sergey Brin,et al.  Extracting Patterns and Relations from the World Wide Web , 1998, WebDB.

[23]  Aron Culotta,et al.  Dependency Tree Kernels for Relation Extraction , 2004, ACL.