“Word sense awareness” is a feature which is not yet implemented in most corpus query tools, Intelligent Computer-Assisted Language Learning (ICALL) environments or computer-readable didactic resources such as graded word lists (Alfter and Graën, 2019; Pilán et al., 2016; Tack et al., 2018). The present paper aims to contribute to filling this lacuna by presenting a word sense disambiguation (WSD) method for ICALL purposes. The method, which is targeted at Spanish as a foreign language (SFL), takes a few prototypical example sentences as input, converts these sentences into “sense vectors”, and integrates part of the training data collection process into interactive vocabulary exercises. The evaluation of the method is based on a selection of 50 ambiguous items related to the domain of economics and compares different types of input data. With a top weighted F1 score of 0.8836, the present study shows that the currently available NLP tools, resources and methods provide all the necessary building blocks for developing a WSD method which can be integrated into interactive ICALL environments.
This paper presents a reflection on the design of an Intelligent Computer-Assisted Language Learning (ICALL) ‘ecosystem’, integrated into an online learning environment for Spanish as a Foreign Language (SFL). The innovative dimension of the ecosystem lies in its triple focus: apart from enabling users to create and use intelligent language learning materials, it also tracks their activities in the environment and provides them insights (e.g. through knowledge clips) into Natural Language Processing (NLP), the source of ICALL’s ‘intelligence’. The reflective analysis is carried out by means of a case study with 32 SFL students, who work with the ecosystem in a blended writing course focused on vocabulary learning, lexical ambiguity, and Word Sense Disambiguation (WSD). Students’ attitudes towards engaging in the ICALL ecosystem are gauged through a questionnaire, which revealed a statistically significant positive change in attitude after having completed the course. However, the results also show that enhanced insights into NLP and increased confidence in the computer as a learning assistant do not necessarily go hand in hand with an increased curiosity and a better user experience.
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