Data-driven Approaches to Learning Phraseology and Formulaic Language: Computation, Co-selection, Contextualization, Cognition
Abstr. due: 01.06.2015
Dates: 18.09.15 — 19.09.15
Area Of Sciences: Humanities;
Organizing comittee e-mail: email@example.com.
Organizers: Iowa State University
The annual Technology for Second Language Learning (TSLL) conference brings together researchers, developers and practitioners who are interested in improvements and innovations in the uses of technology in language learning.
This year, the conference will focus on phraseology and formulaic language. Phraseology and the study of formulaic language encompass any research and teaching practices that explore multi-word units of language between the levels of lexis and syntax. Such units include, among others, fixed strings of words (e.g. ‘academic formulas’, ‘lexical bundles’, ‘ngrams’), more variable strings of abstract form/function items (e.g. ‘collostructions’, ‘semantic sequences’, ‘frames’), textual or mental combinations of individual items or word classes (e.g. ‘collocations’, ‘colligation’, ‘lexical priming’), and terminological units that may vary in meaning depending on the field where they are used (e.g. ‘multi-word expressions’, ‘formulaic sequences’).
The use of technology by corpus linguists to identify formulaic language in large corpora provides new insights into the amount and types of formulaic language people use, while psycholinguistic and cognitive studies investigate the ways multi-word units are acquired, stored in the mental lexicon, and processed by people who communicate in first or second language. Implications of these findings are beginning to be explored for language learning and teaching. Technology-based pedagogy facilitates learners’ access to large collections of relevant texts where they too can explore multi-word units. These new possibilities for the study of language and for learning raise many intriguing questions, such as:
- How are phraseological forms acquired in first or second language, both receptively and productively, and how can this knowledge contribute to designing technology-mediated language learning experiences?
- What innovative computational methods can be used to identify phraseological forms for specific purposes?
- How can computer-assisted language learning (CALL) applications (e.g. vocabulary learning software, automated writing evaluation tools, etc.) benefit from phraseological resources and information?
- How can corpus-derived materials, e.g. lists of phraseological forms, be used to improve language learning experiences?
Conference Web-Site: https://cywrite.engl.iastate.edu/wp/tsll-2015/
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