Using the Sketch Engine for second language learning

Simon Smith & Alice Chen

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Designers of the original Sketch Engine

Adam Kilgarriff, Lexical Computing, Brighton David Tugwell, Tech University, Budapest Pavel Rychly, Masaryk University, Brno Chinese implementation

– Huang Chu-ren and colleagues at Academia Sinica This work

– with Huang Chu-ren and Chen Mei-rong– under NSC grant 95-2411-H-130-005- – the generous support of the NSC is acknowledged

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Facing the problem: lexical choice

“You shall know a word by the company it keeps” (Firth, 1957)

The meaning of face depends on the collocation (詞語搭配 ) – 學漢語的外國人要面對詞語選擇的問題– 許多種動物正在面臨絕種的問題

Similarly with save– Save money– Save life

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Lexical approach to SLA Brain stores linguistic knowledge

– vocab (+features) and a lot of grammar rules– that’s basically what we have to learn

Lewis (1993) and Schmitt (2000) say– the vocab is stored in chunks and collocations – kith is stored with kin– scotch is stored with rumour, and snake, and whisky

Saying strong car or powerful tea or broken house gives away non-native speakers

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From www.teachingenglish.org - a lexical approach activity, based on a story text

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Learners acquire vocabulary in context! ELC teachers believe:

Memorizing vocab doesn’t cut it Consulting a dictionary

– useful for spelling or meaning verification– sparse on real language examples– may yield the wrong sense

Lots of very wide reading– great if you have the time

So, instead…

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Use a corpus tool, like Sketch Engine?

Read, or do homework, as normal Try to guess new words from context, as normal Before using dictionary, go to Sketch Engine

– get a word sketch (short summary) of the new word’s usage

– explore the word’s usage further, by clicking on links in the word sketch

– (demo)

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Limitation of KWIC analysis x As corpora get bigger: too much data

– 50 lines for a word: read all– 500 lines: could read all, takes a long time– 5000 lines: no

Instead, create a statistical summary of word usage– Show most salient 最有顯著性 collocates

(Mutual Information)

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The Sketch Engine x Provides a word sketch

– a corpus-derived one-page summary of a word’s grammatical and collocational behaviour

Implemented for English, Chinese and several other languages

Uses very large corpora

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Functions x KWIC concordance

– Sorting, filtering etc Word sketch Automatic thesaurus Sketch difference

– discriminate near-synonyms

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Experiment: How useful is the Sketch Engine for Chinese learning?

1. Select some intermediate students of Chinese

2. Test their knowledge (pre-test) of collocations in Chinese

3. Ask them to use Sketch Engine regularly in their studies

4. After a period, test them again (post-test)5. Evaluate their progress

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The pre-test Present informants with authentic Chinese

sentences (e.g. from web) One word is missing from the sentence Informant supplies the right word

– by choosing the more likely of two possibilities offered, given the context

English equivalent might be:Steve is a ___ [lovely|sunny] boy, and he works [hard|hardly]

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Sample pre-test questions

1. 上課很認真，下了課也認真，可是她的___一直都不太好。 [ 結果 | 成績 ]

2. 李德朝侵入機場控制區並侵入飛機的行為造成了惡劣的 ___。 [ 結果 | 後果 ]

3. 為什麼我問你的時候，你沒有 ____我這件事？ [ 告訴 | 告知 ]

4. 這個小孩很聰明 , 可是不太守 _____的。 [ 規定 | 規矩 ]

Research questions

What are some problems or limitations for SkE users?

Whether learners feel empowered to explore or use SkE?

How useful can SkE be for CSL learners? How can CSL teachers integrate SkE into

their syllabi?

Background of participants

Attempted to recruit current students– Need incentives budget– Need teachers committed to project

Used volunteers from two discussion boards– Limited number– Asked them to assess their own Chinese

standard

0% 20% 40% 60% 80% 100%

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Profi

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 Procedures A case study -Collect data from the following     Interviews with CSL teachers Observations of target group classes Questionnaire of target groups without using the

Chinese Word Sketch (pre test) pretest target groups-cloze questions and reading

comprehension questions Questionnaire of target groups using the Chinese

Word Sketch (post test) posttest 2 target groups -cloze questions and reading

comprehension questions

Data Analysis

Pre test analysis Post test analysis

Research Results