TAILIEUCHUNG - Báo cáo khoa học: "Bootstrapped Training of Event Extraction Classifiers"

Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requires minimal human supervision. We aim to rapidly train a state-of-the-art event extraction system using a small set of “seed nouns” for each event role, a collection of relevant (in-domain) and irrelevant (outof-domain) texts, and a semantic dictionary. . | Bootstrapped Training of Event Extraction Classifiers Ruihong Huang and Ellen Riloff School of Computing University of Utah Salt Lake City UT 84112 huangrh riloff @ Abstract Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task event extraction systems must be retrained with new annotated data for each domain. In this paper we propose a bootstrapping solution for event role filler extraction that requires minimal human supervision. We aim to rapidly train a state-of-the-art event extraction system using a small set of seed nouns for each event role a collection of relevant in-domain and irrelevant out-of-domain texts and a semantic dictionary. The experimental results show that the bootstrapped system outperforms previous weakly supervised event extraction systems on the MUC-4 data set and achieves performance levels comparable to supervised training with 700 manually annotated documents. 1 Introduction Event extraction systems process stories about domain-relevant events and identify the role fillers of each event. A key challenge for event extraction is that recognizing role fillers is inherently contextual. For example a PERSON can be a perpetrator or a victim in different contexts . John Smith assassinated the mayor vs. John Smith was assassinated . Similarly any COMPANY can be an acquirer or an acquiree depending on the context. Many supervised learning techniques have been used to create event extraction systems using gold standard answer key event templates fortraining . Freitag 1998a Chieu and Ng 2002 Maslennikov and Chua 2007 . However manually generating answer keys for event extraction is time-consuming and tedious. And more importantly event extraction annotations are highly domain-specific so new annotations must be obtained for each domain. The goal of our research is to use bootstrapping techniques to automatically train a .

• Báo cáo khoa học
• Bootstrapped Training
• Event Extraction Classifiers
• Ruihong Huang and Ellen Riloff
• scientific reports
• model language
• process natural language
• Technical efficiency
• Oyster mushroom
• Training course
• Bootstrapped Data Envelopment Analysis
• Irrigation water
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln