tailieunhanh - Báo cáo khoa học: "A Structured Language Model"

The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented. | A Structured Language Model Ciprian Chelba The Johns Hopkins University CLSP Barton Hall 320 3400 N. Charles Street Baltimore MD-21218 Abstract The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model its probabilistic parametrization and a set of experiments meant to evaluate its predictive power are presented. the dog I heard yesterday barked Figure 1 Partial parse Figure 2 A word-parse k-prefix 1 Introduction The main goal of the proposed project is to develop a language model LM that uses syntactic structure. The principles that guided this proposal were the model will develop syntactic knowledge as a built-in feature it will assign a probability to every joint sequence of words-binary-parse-structure the model should operate in a left-to-right manner so that it would be possible to decode word lattices provided by an automatic speech recognizer. The model consists of two modules a next word predictor which makes use of syntactic structure as developed by a parser. The operations of these two modules are intertwined. 2 The Basic Idea and Terminology Consider predicting the word barked in the sentence the dog I heard yesterday barked again. A 3-gram approach would predict barked from heard yesterday whereas it is clear that the predictor should use the word dog which is outside the reach of even 4-grams. Our assumption is that what enables us to make a good prediction of barked is the syntactic structure in the past. The correct partial parse of the word history when predicting barked is shown in Figure 1. The word dog is called the headword of the constituent the dog . and dog is an exposed headword when predicting barked topmost headword in the largest constituent that contains it. The .

• Báo cáo khoa học
• A Structured Language Model
• Ciprian Chelba
• Long Papers
• scientific reports
• model language
• process natural language
• Graph structured Stack
• Natural Language Parsing
• Masaru Tomlta
• A STRUCTURED REPRESENTATION
• WORD SENSESIR
• SEMANTIC ANALYSIS
• Database processing
• Structured query language
• The relational model
• Database design using normalization
• The entity relationship model
• Transforming data models
• Lecture Database System
• Database System
• Entity relationship model
• Relational model
• Structured query language (SQL)
• Database design theory
• 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
• 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