Authors:
Vera Sheinman
and
Takenobu Tokunaga
Affiliation:
Tokyo Institute of Technology, Japan
Keyword(s):
Adjective-scales, Patterns, Web-based, Second language learning, Gradation, Computational linguistics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Authoring Tools and Content Development
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Ontologies and Meta-Data Standards
;
Web-Based Learning, Wikis and Blogs
Abstract:
In this study we introduce AdjScales, a method for scaling similar adjectives by their strength. It combines existing Web-based computational linguistic techniques in order to automatically differentiate similar adjectives that describe the same property by strength. Though this kind of information is rarely present in most of the lexical resources and dictionaries, it might be useful for language learners that try to distinguish between similar words and that want to capture the differences from a single structure. Additionally, AdjScales might be used by constructors of lexical resources in order to enrich them. The method is evaluated by comparison with annotation on a subset of adjectives from WordNet by four native English speakers. The collected annotation is an interesting resource by its own right. This work is a first step towards automatic differentiation of meaning between similar words for learners.