Facilitating academic words learning: a data-driven approach using a collocation consultation system built from open access research papers Article Swipe
It is essential and beneficial for ESP students to master collocations of a set of core academic words. Corpus analysis tools (e.g. concordancers) have been widely used in facilitating collocation learning, and promising results have been demonstrated in the literature. This paper presents a learner friendly collocation consultation system built from 50,000 open access research papers made available by CORE (https://core.ac.uk/). The research papers are grouped into four disciplines: Arts and Humanities, Physical Sciences, Life Sciences and Social Sciences. From these articles, useful syntactic-based word combinations (e.g., verb+noun, noun+noun, adjective+noun) are extracted, organized by syntactic patterns, sorted by frequency, and linked to their context sentences. Learners can search collocations and look up the usage of an academic word in any of these four disciplines by simply entering the word or selecting it from one of pre-compiled academic word lists. The paper will also show how the system was used in an initial study carried out with 15 international students studying computer science at University of Waikato, New Zealand.
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Metadata
- Type
- article
- Language
- en
- http://researcharchive.wintec.ac.nz/6551/3/Alex_Yu_certificate_participants2018.pdf
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2955773227
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2955773227Canonical identifier for this work in OpenAlex
- Title
-
Facilitating academic words learning: a data-driven approach using a collocation consultation system built from open access research papersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-12-07Full publication date if available
- Authors
-
Alex YuList of authors in order
- PDF URL
-
https://researcharchive.wintec.ac.nz/6551/3/Alex_Yu_certificate_participants2018.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://researcharchive.wintec.ac.nz/6551/3/Alex_Yu_certificate_participants2018.pdfDirect OA link when available
- Concepts
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Collocation (remote sensing), Noun, Adjective, Computer science, Natural language processing, Context (archaeology), Verb, Artificial intelligence, Linguistics, Set (abstract data type), Word (group theory), Paleontology, Machine learning, Biology, Philosophy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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