tailieunhanh - Báo cáo khoa học: "Combining a Statistical Language Model with Logistic Regression to Predict the Lexical and Syntactic Difficulty of Texts for FFL"

Reading is known to be an essential task in language learning, but finding the appropriate text for every learner is far from easy. In this context, automatic procedures can support the teacher’s work. Some tools exist for English, but at present there are none for French as a foreign language (FFL). In this paper, we present an original approach to assessing the readability of FFL texts using NLP techniques and extracts from FFL textbooks as our corpus. Two logistic regression models based on lexical and grammatical features are explored and give quite good predictions on new texts. . | Combining a Statistical Language Model with Logistic Regression to Predict the Lexical and Syntactic Difficulty of Texts for FFL Thomas L. Francois Aspirant FNRS CENTAL Center for Natural Language Processing Universite catholique de Louvain 1348 Louvain-la-Neuve Belgium Abstract Reading is known to be an essential task in language learning but finding the appropriate text for every learner is far from easy. In this context automatic procedures can support the teacher s work. Some tools exist for English but at present there are none for French as a foreign language FFL . In this paper we present an original approach to assessing the readability of FFL texts using NLP techniques and extracts from FFL textbooks as our corpus. Two logistic regression models based on lexical and grammatical features are explored and give quite good predictions on new texts. The results shows a slight superiority for multinomial logistic regression over the proportional odds model. 1 Introduction The current massive mobility of people has put increasing pressure on the language teaching sector in terms of the availability of instructors and suitable teaching materials. The development of Intelligent Computer Aided Language Learning ICALL has helped both these needs while the Internet has increasingly been used as a source of exercises. Indeed it allows immediate access to a huge number of texts which can be used for educational purposes either for classical reading comprehension tasks or as a corpus for the creation of various automatically generated exercises. However the strength of the Internet is also its main flaw there are so many texts available to the teacher that he or she can get lost. Having gathered some documents suitable in terms of subject matter teachers still have to check if their readability levels are suitable for their students a highly time-consuming task. This is where NLP applica tions able to classify documents according to their .

• Báo cáo khoa học
• Combining a Statistical Language Model
• Logistic Regression to Predict the Lexical
• Syntactic Difficulty of Texts for FFL
• 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