Automatic presentation of sense-specific lexical information in an intelligent learning system
Learning vocabulary and understanding texts present difficulty for language learners due to, among other things, the high degree of lexical ambiguity. By developing an intelligent tutoring system, this dissertation examines whether automatically providing enriched sense-specific information is effective for vocabulary learning and reading comprehension of second language learners. The system developed in this study contributes to an extended understanding of how NLP techniques can be applied more effectively in an educational environment.The system allows learners to upload texts and click on any content word in order to obtain sense-appropriate lexical information for unfamiliar or unknown words during reading. The system consists of three components: (1) the system manager controls the interaction among each learner, the NLP server, and the lexical database; (2) the NLP server converts a raw input text to a linguistically-analyzed text; (3) the lexical database is used to provide a sense-appropriate definition and example sentences of a word to the learner. To obtain the sense-appropriate information, the system first performs word sense disambiguation (WSD) on the input text. Pointing to appropriate examples tuned for language learners, however, is complicated by the fact that the database of examples is from one repository (COBUILD), while automatic WSD systems generally rely on senses from another (WordNet). The lexical database, then, is indexed by WordNet senses, each of which points to an appropriate corresponding COBUILD sense. The fact that every sense inventory has its own standards of sense distinction poses a serious problem in integrating these inventories into one. To redirect an input WordNet sense to a corresponding COBUILD sense, thus, a word sense alignment algorithm was developed, following a heuristic of favoring flatter alignment structures.With this system, an empirical study was conducted with 60 intermediate learners of English as a second language to examine whether this system can lead learners to improve their vocabulary acquisition and reading comprehension. The findings show that learners demonstrated higher performance when receiving sense-specific information. Furthermore, the qualitative examination of the effect of automatic system errors show that, although learners showed learning regardless of the appropriateness of lexical information, they still showed relatively greater learning when given appropriate lexical information.
English as a second language; intelligent computer assisted language learning; vocabulary learning and reading comprehension; word sense alignment; word sense disambiguation; Linguistics; Educational technology; English language; Study and teaching; Foreign speakers; Linguistics; Educational technology; English as a second language;