Peeling Back the Layers: Detecting Event Role Fillers in Secondary Contexts

The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multi-layered event extraction architecture that progressively "zooms in" on relevant information. Our extraction model includes a document genre classifier to recognize event narratives, two types of sentence classifiers, and noun phrase classifiers to extract role fillers. These modules are organized as a pipeline to gradually zero in on event-related information. We present results on the MUC-4 event extraction data set and show that this model performs better than previous systems.

[1]  Claire Cardie,et al.  University of Massachusetts: Description of the CIRCUS System as Used for MUC-3 , 1991, MUC.

[2]  Douglas E. Appelt,et al.  FASTUS: A Finite-state Processor for Information Extraction from Real-world Text , 1993, IJCAI.

[3]  Ellen Riloff,et al.  Automatically Constructing a Dictionary for Information Extraction Tasks , 1993, AAAI.

[4]  Dan I. Moldovan,et al.  Acquisition of semantic patterns for information extraction from corpora , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

[5]  David Fisher,et al.  CRYSTAL: Inducing a Conceptual Dictionary , 1995, IJCAI.

[6]  Scott B. Huffman,et al.  Learning information extraction patterns from examples , 1995, Learning for Natural Language Processing.

[7]  Ellen Riloff,et al.  Automatically Generating Extraction Patterns from Untagged Text , 1996, AAAI/IAAI, Vol. 2.

[8]  Dayne Freitag,et al.  Multistrategy Learning for Information Extraction , 1998, ICML.

[9]  Lynette Hirschman,et al.  The Evolution of evaluation: Lessons from the Message Understanding Conferences , 1998, Comput. Speech Lang..

[10]  Dayne Freitag,et al.  Toward General-Purpose Learning for Information Extraction , 1998, ACL.

[11]  Ellen Riloff,et al.  Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping , 1999, AAAI/IAAI.

[12]  Andrew McCallum,et al.  Information Extraction with HMM Structures Learned by Stochastic Optimization , 2000, AAAI/IAAI.

[13]  Ralph Grishman,et al.  Automatic Acquisition of Domain Knowledge for Information Extraction , 2000, COLING.

[14]  Fabio Ciravegna,et al.  Adaptive Information Extraction from Text by Rule Induction and Generalisation , 2001, IJCAI.

[15]  Dan Roth,et al.  Relational Learning via Propositional Algorithms: An Information Extraction Case Study , 2001, IJCAI.

[16]  Hwee Tou Ng,et al.  A maximum entropy approach to information extraction from semi-structured and free text , 2002, AAAI/IAAI.

[17]  Ralph Grishman,et al.  An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition , 2003, ACL.

[18]  Raymond J. Mooney,et al.  Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction , 2003, J. Mach. Learn. Res..

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

[20]  Aidan Finn,et al.  Multi-level Boundary Classification for Information Extraction , 2004, ECML.

[21]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[22]  S. Sathiya Keerthi,et al.  A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..

[23]  Kun Yu,et al.  Resume Information Extraction with Cascaded Hybrid Model , 2005, ACL.

[24]  Kalina Bontcheva,et al.  Using Uneven Margins SVM and Perceptron for Information Extraction , 2005, CoNLL.

[25]  Mark Stevenson,et al.  A Semantic Approach to IE Pattern Induction , 2005, ACL.

[26]  Nick Cercone,et al.  Segment-Based Hidden Markov Models for Information Extraction , 2006, ACL.

[27]  Satoshi Sekine,et al.  On-Demand Information Extraction , 2006, ACL.

[28]  Satoshi Sekine,et al.  Preemptive Information Extraction using Unrestricted Relation Discovery , 2006, NAACL.

[29]  Tat-Seng Chua,et al.  A Multi-resolution Framework for Information Extraction from Free Text , 2007, ACL.

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

[31]  Siddharth Patwardhan,et al.  Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions , 2007, EMNLP.

[32]  Heng Ji,et al.  Refining Event Extraction through Cross-Document Inference , 2008, ACL.

[33]  Siddharth Patwardhan,et al.  A Unified Model of Phrasal and Sentential Evidence for Information Extraction , 2009, EMNLP.

[34]  Ralph Grishman,et al.  Using Document Level Cross-Event Inference to Improve Event Extraction , 2010, ACL.

[35]  Ellen Riloff,et al.  An Introduction to the Sundance and AutoSlog Systems , 2011 .