A WordNet-Based Near-Synonyms and Similar-Looking Word Learning System

Near-Synonyms and Similar-Looking (NSSL) words can create confusion for English as Foreign Language Learners as a result of a type of lexical error that often occurs when they confuse similar-looking words that are near synonyms to have the same meaning. Particularly, this may occur if the similar-looking words have the same translated meaning. This study proposes a method to find these NSSL words and designed three experiments to investigate whether NSSL matching exercises could increase Chinese EFL learners' awareness of NSSL words. Three primary findings arose from the study. First, a performance evaluation of the experiment showed good results and determined that the method extracted suitable NSSL words whose meaning EFL learners may confuse. Secondly, the analysis results of the evaluation of Computer Assisted Language Learning (CALL) software showed that this system is practical for language learning, but lacks authenticity. Thirdly, a total of ninety-two Chinese students participated in this study and the findings indicated that students increased awareness of NSSL words and improved in ability of NSSL word distinction while still maintaining the knowledge one month after they had completed the matching exercises. Additionally, students’ feedback expressed that they had benefited from discovery learning and that they thought it was not difficult to discover the differences among NSSL words. Further research might extend the method proposed in this study to distracter choice of automatic question generation.

[1]  Nick C. Ellis,et al.  Sequencing in SLA , 1996, Studies in Second Language Acquisition.

[2]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[3]  Eric Brill,et al.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[4]  J. Baron,et al.  Use of orthographic and word-specific knowledge in reading words aloud. , 1976 .

[5]  Yeung,et al.  Cognitive Load and Learner Expertise: Split-Attention and Redundancy Effects in Reading with Explanatory Notes , 1998, Contemporary educational psychology.

[6]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[7]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[8]  Tom Cobb,et al.  Is There Any Measurable Learning from Hands-On Concordancing?. , 1997 .

[9]  J. Richards.  The Role of Vocabulary Teaching. , 1976 .

[10]  Ovid J. L. Tzeng,et al.  Visual lateralisation effect in reading Chinese characters , 1979, Nature.

[11]  Eiichiro Sumita,et al.  Measuring Non-native Speakers’ Proficiency of English by Using a Test with Automatically-Generated Fill-in-the-Blank Questions , 2005 .

[12]  Susumu Kunifuji,et al.  A Divergent-Style Learning Support Tool for English Learners Using a Thesaurus Diagram , 2006, KES.

[13]  Yu-Lung Wu,et al.  A Practical Computer Adaptive Testing Model for Small-Scale Scenarios , 2008, J. Educ. Technol. Soc..

[14]  劉顯親,et al.  A Study of Using Web Concordancing for English Vocabulary Learning in a Taiwanese High School Context , 2003 .

[15]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[16]  Chao-Ming Cheng,et al.  Lateralization in the visual perception of Chinese characters and words , 1989, Brain and Language.

[17]  Noriko Nagata,et al.  THE EFFECTIVENESS OF COMPUTER-ASSISTED INTERACTIVE GLOSSES , 1999 .

[18]  Jerome S. Bruner,et al.  The relevance of education , 1971 .

[19]  Maxine Eskénazi,et al.  Automatic Question Generation for Vocabulary Assessment , 2005, HLT.

[20]  George A. Miller,et al.  Nouns in WordNet: A Lexical Inheritance System , 1990 .

[21]  A. Bernstein,et al.  SimPack: A Generic Java Library for Similarity Measures in Ontologies , 2005 .

[22]  Youngkyun Baek,et al.  Improving Recall and Transfer Skills Through Vocabulary Building in Web-Based Second Language Learning: An Examination by Item and Feedback Type , 2008, J. Educ. Technol. Soc..

[23]  Chih-Ming Chen,et al.  Personalized Intelligent M-learning System for Supporting Effective English Learning , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[24]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[25]  Hiroshi Nakagawa,et al.  A Cloze Test Authoring System and Its Automation , 2007, ICWL.

[26]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

[27]  M. Martin.  Advanced Vocabulary Teaching: The Problem of Synonyms* , 1984 .

[28]  Eric Brill,et al.  Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.

[29]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.