TAILIEUCHUNG - Báo cáo khoa học: "HOW TO DETECT GRAMMATICAL ERRORS IN A TEXT WITHOUT PARSING IT"

The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a firat-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text. This may seem to be an intuitively implausible and theoretically inadequate model of natural language syntax, but nevertheless it can successfully pinpoint most grammatical errors in a text. Several modifications to CLAWS have been explored | HOW TO DETECT GRAMMATICAL ERRORS IN A TEXT WITHOUT PARSING IT Eric Steven Atwell Artificial Intelligence Group Department of Computer Studies Leeds University Leeds LS2 9JT . EARN BITNET eric ABSTRACT The Constituent Likelihood Automatic Word-tagging System CLAWS was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse but uses a first-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detea grammatical errors essentially by flagging unlikely word-class transitions in the input text This may seem to be an intuitively implausible and theoretically inadequate model of natural language syntax but nevertheless it can successfully pinpoint most grammatical errors in a text Several modifications to CLAWS have been explored. The resulting system cannot detect all errors in typed documents but then neither do far more complex systems which attempt a full parse requiring much greater computation. Checking Grammar in Texts A number of researchers have experimented with ways to cope with grammatically ill-formed English input for example Carbonell and Hayes 83 Chamiak 83 Granger 83 Hayes and Mouradian 81 Heidom et al 82 Jensen et al 83 Kwasny and Sondheimer 81 Weischedel and Black 80 Weischedel and Sondheimer 83 . However the majority of these systems are designed for Natural Language interfaces to software systems and so can assume a restriaed vocabulary and syntax for example the system discussed by Fass 83 had a vocabulary of less than 50 words. This may be justifiable for a NL front-end to a computer system such as a Database Query system since even an artificial subset of English may be more acceptable to users than a formal command or query language. However for automated text-checking in Word Processing we cannot reasonably ask the wp user to restrict their English text in this way. This means that WP .

• Báo cáo khoa học
• HOW TO DETECT GRAMMATICAL ERRORS
• TEXT WITHOUT PARSING IT
• Eric Steven Atwell
• 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