Active learning for relation type extension with local and global data views

Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.

[1]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[2]  Zhu Zhang,et al.  Weakly-supervised relation classification for information extraction , 2004, CIKM '04.

[3]  Jing Jiang,et al.  Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction , 2009, ACL.

[4]  Craig A. Knoblock,et al.  Selective Sampling with Redundant Views , 2000, AAAI/IAAI.

[5]  Ralph Grishman,et al.  Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters , 2010, COLING.

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

[7]  Scott Miller,et al.  Name Tagging with Word Clusters and Discriminative Training , 2004, NAACL.

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

[9]  Ralph Grishman,et al.  Semi-supervised Relation Extraction with Large-scale Word Clustering , 2011, ACL.

[10]  ChengXiang Zhai,et al.  A Systematic Exploration of the Feature Space for Relation Extraction , 2007, NAACL.

[11]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[12]  Ralph Grishman,et al.  Extracting Relations with Integrated Information Using Kernel Methods , 2005, ACL.

[13]  Guodong Zhou,et al.  Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information , 2007, EMNLP.

[14]  Zornitsa Kozareva,et al.  Not All Seeds Are Equal: Measuring the Quality of Text Mining Seeds , 2010, NAACL.

[15]  Dan Roth,et al.  Exploiting Background Knowledge for Relation Extraction , 2010, COLING.

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

[17]  Jian Su,et al.  Exploring Various Knowledge in Relation Extraction , 2005, ACL.

[18]  Dekang Lin,et al.  Phrase Clustering for Discriminative Learning , 2009, ACL.

[19]  Eric Crestan,et al.  Helping editors choose better seed sets for entity set expansion , 2009, CIKM.

[20]  Dmitry Zelenko,et al.  Kernel methods for relation extraction , 2003 .