Corpus Linguistics Conference 2015

Corpus Linguistics #cl2015: notes and pics

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Corpus Linguistics Conference 2015, University of Lancaster, UK Thanks to @TonyMcEnery, @HardieResearch and everybody at @UCREL_Lancaster for organizing a wonderful conference. Abstract book download: A selection of talks and personal notes: Learner corpus research plenary #cl2015 Multi-dimensional analysis of oral proficiency interviews #cl2015 Non-obvious meaning in CL and CADS #cl2015 Representation of benefit claimants in … Read more

Multi-dimensional analysis of oral proficiency interviews #cl2015

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  Shelley Staples; Jesse Egbert; Geoff LaFlair A multi-dimensional comparison of oral proficiency interviews to conversation, academic and professional spoken registers MELAB : Michigan Engish Language Battery 989 OPIs in 2013 OPI used for academic and profesional purposes Only transcribed the first 5 minutes 55 linguistic features TagCount FA 6 factor solution Dimensions interpreted functionally Dimension scores Differences across … Read more

Non-obvious meaning in CL and CADS #cl2015

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Plenary session: Alan Partington Non-obvious meaning in CL and CADS: from ‘hindsight post-dictability’ to sweet serendipity Chair: Amanda Potts http://www3.lingue.unibo.it/blog/clb/ Introspection & intuition Processes of inference from the linguistic trace left by speakers/writers Shared meaning Idiom principle Complexity of common grammatical items Colligation: every word primed to occur in or avoid certain grammatical positions and … Read more

Representation of benefit claimants in UK media #cl2015

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  Ben Clarke The ideological representation of benefit claimants in UK print media 2010 – 2014 2.3 M corpus benefits clsimant(s) search criteria Adjectival constructions Adjective lemmas are ranked hard number 40 tough number 53 enTenTen13 score Tough on is significant in the corpus Tough patterns Benefit claimants: scroungers tougher conditions, curbs on Prepositions and … Read more

Tono Linguistic feature extraction #cefr #cl2015

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Yukio Tono Linguistic feature extraction and evaluation using machine learning to identify “criterial” grammar constructions for the CEFR levels   L2 learner profile English Profile – CEFR for Englsih Criterial features: Hawkins & Filipovic 2012 CEFR-J RLD Project: aim prepare list of vocabulary and grammar item to be taught and assessed at each CEFR level CEFR … Read more