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Corpora in lexical studies

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1 Corpora in lexical studies
Corpus Linguistics Richard Xiao

2 Aims of this session Lecture Lab session Corpus-based lexicography
Collocation and colligation Lab session Collocation using WST Collocation using AntConc Collocation and colligation in Xaira Using the BNCweb to study collocation

3 Corpus revolution in lexicographic and lexical studies
Lexicographic and lexical studies are the greatest beneficiaries of corpora Corpora have “revolutionised” dictionary making and reference publishing It is now nearly unheard of for new dictionaries and new editions of old dictionaries published from the 1990s onwards not to claim to be based on corpus data

4 Why use corpora in dictionary making?
Machine-readable corpora allow dictionary makers to extract all authentic, typical examples of the usage of a lexical item from a large body of text in a few seconds Corpora allow dictionary makers to select entries based on frequency information Corpora can readily provide frequency information and collocation information for readers Textual (e.g. register, genre and domain) and sociolinguistic (e.g. user gender and age) information encoded in corpora allows lexicographers to give a more accurate description of the usage of a lexical item

5 Why use corpora in dictionary making?
Corpus annotations such as part-of-speech tagging and word sense disambiguation also enable a more sensible grouping of words which are polysemous and homographs A “monitor corpus” allows lexicographers to track subtle change in the meaning and usage of a lexical item so as to keep their dictionaries up-to-date Corpus evidence can complement or refute the intuitions of individual lexicographers, which are not always reliable because of potential biases in intuitions

6 Five emphases Changes brought about by corpora to dictionaries and other reference books - five “emphases” (Hunston 2002) an emphasis on frequency an emphasis on collocation and phraseology an emphasis on variation an emphasis on lexis in grammar an emphasis on authenticity

7 Top 1000 written / spoken words
Authentic examples

8 Corpus-based learner dictionaries
First ‘fully corpus-based’ dictionary Collins Cobuild English Dictionary (1987) Some corpus-based learner dictionaries Longman Dictionary of Contemporary English (3rd edition) Oxford Advanced Learner’s Dictionary (OALD, 5th edition) Cambridge International Dictionary of English (1st edition)

9 Frequency dictionaries

10 Collocation Collocation is among the linguistic concepts which have benefited most from advances in corpus linguistics What is collocation? strong tea, powerful car (Halliday 1976) “collocations of a given word are statements of the habitual or customary places of that word…the company that words keep” (Firth 1968:181-2) “One of the meanings of night is its collocability with dark” (Firth 1957:196) “a frequent co-occurrence of two lexical items in the language” (Greenbaum 1974:82) expel a school child vs. cashier an army officer “I propose to bring forward as a technical term, meaning by collocation, and apply the test of collocability” (Firth 1957: 194)

11 Meaning by collocation
“There is frequently so high a degree of interdependence between lexemes which tend to occur in texts in collocation with one another that their potentiality for collocation is reasonably described as being part of their meaning” (Lyons 1977: 613) Complete description of the meaning of a word would have to include the other word or words that collocate with it “You shall know a word by the company it keeps!” (Firth 1968:179) Collocation is part of the word meaning

12 Two types of collocation
Coherence collocation vs. neighbourhood (horizontal) collocation (Scott 1998) Coherence collocation Collocates associated with a word (e.g. letter – stamp, post office) Neighbourhood collocation Words which do actually co-occur with the word (letter - my, this, a, etc)

13 Coherence collocation
“A cover term for the cohesion that results from the co-occurrence of lexical items that are in some way or other typically associated with one another, because they tend to occur in similar environments.” (Halliday & Hasan 1976:287) candle – flame – flicker hair – comb – curl – wave sky – sunshine – cloud – rain Difficult to measure using a statistical formula

14 Neighbourhood collocation
Collocation in corpus linguistics Structure of collocation – collocation window “We may use the term node to refer to an item whose collocations we are studying, and we may then define a span as the number of lexical items on each side of a node that we consider relevant to that node. Items in the environment set by the span we will call collocates.” (Sinclair 1966:415) Casual vs. significant collocation Significant collocation: collocation that occurs more frequently than would be expected (in a statistical sense) on the basis of the individual items n.b. Neighbourhood (horizontal) collocations can include some coherence collocations

15 Intuition vs. collocation
Greenbaum (1974): “people disagree on collocations” in introspection-based elicitation experiments Although “collocation can be observed informally” on the basis of intuitions, “it is more reliable to measure it statistically, and for this a corpus is essential” (Hunston 2002: 68) Intuition is often a poor guide to collocation “because each of us has only a partial knowledge of the language, we have prejudices and preferences, our memory is weak, our imagination is powerful (so we can conceive of possible contexts for the most implausible utterances), and we tend to notice unusual words or structures but often overlook ordinary ones” (Krishnamurthy 2000: 32-33) Collocation can be measured on the basis of co-occurrence statistics (MI, z, t, LL etc) – more discussion to follow

16 Collocation is syntagmatic
Langue (Language system) paradigmatic famous boots. On the stroke of full time the Stoke the lead on the stroke of half-time with a goal Smith sin-binned on the stroke of half-time, added a clinched their win on the stroke of lunch after resuming chase by declaring on the stroke of lunch. <p> With a lead expectant crowd, on the stroke of midday. The bird hour began not upon the stroke of midnight but upon the of midnight but upon the stroke of noon. There was, booked in advance. On the stroke of seven, a gong summons Promptly on the stroke of six 'clock, the chooks from Edinburgh on the stroke of the Millennium. Parole (Utterance) syntagmatic

17 Collocation vs. colligation
Relationship between a lexical item and other lexical items Relationship between words at the lexical level E.g. very collocates with good Colligation Relationship between a lexical item and a grammatical category Relationship between words at the grammatical level E.g. very colligates with ADJ

18 WST Collocate settings
Concord tab

19 WST collocates Strength of relationship is displayed as if it hasn't yet been computed

20 Strength of collocation relationship
A wordlist is required

21 Highlight and double click…

22 …to see the selected collocate

23 Collocates in AntConc

24 Collocation in Xaira

25 Colligation in Xaira

26 Exploring collocation with BNCweb

27 Search for “sweet”

28 Concordances of “sweet”
KWIC view

29 KWIC view

30 Dropdown menu: collocations

31 Collocation setting

32 Collocation database (default settings)

33 Adjusting settings

34 Noun collocates of “sweet”
Click on a word to see its collocation info

35 Collocation info of “sweet” + “smell”
Click on a number to see concordances of collocates at that position

36 Concordances of “smell” at R2

37 Collocation statistics

38 Rank by frequency “Sweet Maxwell” is a personal name. Frequent words crowd into the top of the collocate list: Are they genuine collocates?

39 Rank by the t test Also focusing on frequent words?

40 Rank by MI Infrequent words at the top of the list
n.b. - “Sweet Afton” is a phrase from the lyrics expressing the beauty of the River Afton; “sweet nothings” means romantic and loving talks between sweethearts; “sweet marjoram” is the name of a herb for cooking. Infrequent words at the top of the list How useful are they (especially to English learners)?

41 Rank by the z score Like MI, the z score also over-estimates infrequent items (e.g. nothings, afton, marjoram)

42 Log-likelihood test

43 Rank by MI3

44 Rank by dice coefficient

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