TAILIEUCHUNG - Báo cáo khoa học: "Discriminative Sentence Compression with Soft Syntactic Evidence"

We present a model for sentence compression that uses a discriminative largemargin learning framework coupled with a novel feature set defined on compressed bigrams as well as deep syntactic representations provided by auxiliary dependency and phrase-structure parsers. The parsers are trained out-of-domain and contain a significant amount of noise. We argue that the discriminative nature of the learning algorithm allows the model to learn weights relative to any noise in the feature set to optimize compression accuracy directly. This differs from current state-of-the-art models (Knight and Marcu, 2000) that treat noisy parse trees, for both compressed and uncompressed sentences,. | Discriminative Sentence Compression with Soft Syntactic Evidence Ryan McDonald Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 ryantm@ Abstract We present a model for sentence compression that uses a discriminative large-margin learning framework coupled with a novel feature set defined on compressed bigrams as well as deep syntactic representations provided by auxiliary dependency and phrase-structure parsers. The parsers are trained out-of-domain and contain a significant amount of noise. We argue that the discriminative nature of the learning algorithm allows the model to learn weights relative to any noise in the feature set to optimize compression accuracy directly. This differs from current state-of-the-art models Knight and Marcu 2000 that treat noisy parse trees for both compressed and uncompressed sentences as gold standard when calculating model parameters. 1 Introduction The ability to compress sentences grammatically with minimal information loss is an important problem in text summarization. Most summarization systems are evaluated on the amount of relevant information retained as well as their compression rate. Thus returning highly compressed yet informative sentences allows summarization systems to return larger sets of sentences and increase the overall amount of information extracted. We focus on the particular instantiation of sentence compression when the goal is to produce the compressed version solely by removing words or phrases from the original which is the most common setting in the literature Knight and Marcu 2000 Riezler et al. 2003 Turner and Charniak 2005 . In this framework the goal is to find the shortest substring of the original sentence that conveys the most important aspects of the meaning. We will work in a supervised learning setting and assume as input a training set T xt yt of original sentences xt and their compressions yt. We use the Ziff-Davis corpus which is a .

• Báo cáo khoa học
• Discriminative Sentence Compression
• Soft Syntactic Evidence
• Ryan McDonald
• scientific reports
• model language
• process natural language
• 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