Concordances, collocations and connotation

1
Concordances, collocations and connotation

• Barnbrook G (1996) Language and Computers.
  Edinburgh EUP. Chapters 3,4,5
• Partington A (1998) Patterns and Meanings.
  Amsterdam John Benjamins. Chapters 1,2,4

2
Lexical information in corpora

• Start looking at the kind of information (about
  individual words) that can be got from corpora
• Simple frequency information
• Distribution information
• Collocation (co-occurrence information)
• Connotation (semantic prosody)
• Introduce basic ideas
• Future topics
• Statistics
• Case studies

3
Frequency information

• Most banal information counting how many times a
  word (type) appears in a text
• Most frequent words will be function words, so
  often f counts exclude words listed in a stop
  list
• Should you count words or lemmas?
• Should you distinguish alternate meanings of
  ambiguous word forms (if you can)?

4
Frequency information

• Frequency information on its own is not
  particularly interesting
• Quite useful to compare f of related words
• eg alternative readings of a given word form
  (already seen in probability calculations in
  tagging)
• or comparing near synonyms, especially if we can
  take context into account (see later)
• f of a given word in a given context can be
  indicative, eg pronouns more frequent as subject
  or 1st word of sentence

5
Types and tokens

• Remember distinction between tokens (words) and
  types (different words)
• Type count gives a measure of how many DIFFERENT
  words are used
• Type-token ratio gives a measure of vocabulary
  richness
• If vocabulary is very varied, TTR will be higher
• TTR is very sensitive to overall text length, so
  it is not meaningful to compare TTRs for texts of
  different lengths
• Standardized TTR is the average of the TTR for
  each sequence of n words (typical default n1000)
  in a text or corpus

6
Vocabulary growth curve

• Plotting types against tokens for a given text
  shows us how the TTR grows as the text gets
  longer
• Typically, the curve starts steeply and then
  flattens, sooner or later reflecting homogeneity
  (or otherwise) of the text

VGC for Macbeth in Basic English source
http//web.missouri.edu/youmansc/vmp/help/Youmans
-TypeToken.pdf
7
Vocabulary growth curve

• Comparative VGC for four texts
• Simple measure used in some literary studies

(a)
(b)
(c)
(d)
(a) Longfellow (b) Hemingway (c) Basic English
(Macbeth) (d) Bible (Genesis 2)
8
Vocabulary in context

• Concordance, also known as KWIC list (key word
  in context)
• Allows us to see the (immediate) environment in
  which a word appears
• Listings can be customised to show what you want
  more clearly, eg
• sorted according to next or previous word
• showing more or less context

9
source A Partington Patterns and Meanings.
Amsterdam (1998) John Benjamins
10
CIWK search

• inverted KWIC
• specify the context and look to see what words
  occur in it

11
Collocation

• Term coined by J R Firth (1957) to characterise
  (part of) his theory of meaning
• You shall judge a word by the company it keeps
• The occurrence of two or more words within a
  short space of each other in a text (Sinclair
  1991)
• The relationship a lexical item has with items
  tha appear with greater than random probability
  in its (textual) context (Hoey 1991 emphasis
  added)

12
Collocation, text type and style

• Distinguish between general and more usual
  collocations vs technical and more personal ones
• eg in a general corpus time collocates with save,
  spend, waste, fritter away,
• but in a corpus of sports reports time collocates
  with half, full, extra, injury, first, second,
  third,

13
Collocation and idiom

• Listing collocations will often reveal idioms and
  cliches
• Important to think of collocation as extending
  beyond neighbouring words (which can be captured
  by simple concordances)

14
Collecting collocations

• If we are to look beyond neighbouring words, what
  constraints might we impose?
• Collocation means co-occurrence within some
  defined context
• possibly a window of n words to left and/or
  right
• if corpus is tagged/parsed, we can look at
  collocations within structures
• or we can define the window in terms of
  constituents rather than words

15
Measuring significance

• The significance of any co-occurrences nees to be
  established
• Raw co-occurrence frequency counts mean nothing
• Need to be compared to something else
• Need to compare a given co-occurrence with random
  chance, or with some other co-occurrence
• More detail next time

16
Collocation and synonymy

• Collocation is good evidence in discussing (near)
  synonymy
• Lots of studies take near synonyms and look to
  see if the nature of their relationship can be
  characterised by their distribution
• In other words what words does each of the
  synonym set collocate with?
• Especially useful for language learners

17
Example of sheer and synonyms

• (from Partington book)
• three senses (LDOCE)
• pure, nothing but, eg sheer luck
• steep, sheer drop
• thin, sheer stockings
• (Cobuild) use sheer to emphasize completeness of
  state
• 92 occurrences of sheer (in meaning 1) in his
  corpus

18
collocations of sheer

• expression of magnitude of weight or volume to
  right (20)
• volume, weight, numbers, mass, scale, quantity,
  size
• almost always with article the
• expression of force, strength or energy (22)
• energy. exertion, force, muscle, strength, power,
  pressure, fury, pace, intensity
• usually with the, or a preposition but no article
• expression of persistence (14)
• pesistence, irreversibility, obstinacy,
  indomitability, insistence, reliability,
  integrity, hard work
• left context through, because of, out of,
  expressing causation, but not the

19
collocations of sheer

• nouns expressing strong emotion (11)
• fun, joy, panic, inspiration, enjoyment, terror
• nouns expressing extreme personal qualities (11)
• beauty, glamour, brutality, thuggery, madness,
  folly
• nouns expressing extreme ability or lack of same
  (8)
• expertise, competence, virtuosity, gamesmanship

20
Synonyms of sheer - pure

• LDOCE definitions, 5 meanings of which two
  overlap
• not mixed with anything
• complete, thorough
• Corpus has 135 examples
• Larger variety of syntactic environments (sheer
  was always modifying a noun) including
  predicative, which sheer does not occur in
• ? The drop was sheer
• His fury was sheer

21
Synonyms of sheer - pure

• Religious-moral context sense of unmixed
• doctrine, faith, goodness chemicals, gold
• But, many examples where it has an emphasizing
  function, like sheer
• accident, chance, comedy, guesswork, honesty,
  idiocy, malice, nostalgia, pleasure, selfishness,
  talent, theatre, vulnerability, whim, wickedness
• often with proper nouns (unlike sheer)
• No examples of pure collocating with items
  expressing magnitude, force or persistence
• Some overlap with sheer
• personal qualities, emotion (though generally
  less extreme ones)
• Only few examples of pure in prepositional phrase
  expressing causation causes can be sheer, but
  states are pure

22
Other synonyms of sheer

• Partington does similar analysis of complete and
  absolute
• Shows that each of the synoynms has more
  typical uses and patterns, though there is some
  overlap
• But there is also clear evidence of complementary
  usage

23
Connotation and semantic prosody

• Collocation can also be used to illustrate
  connotation
• secondary implications of a word (Lyons 1977)
• Three distinct uses of the term
• marker of a particular speech variety (eg lovely)
• cultural implications (words used to describe
  women show what society thinks of them)
• marker of speakers evaluation (firm stubborn)
• Semantic prosody (Sinclair 1987)
• use of a certain word spreads its connotation
  over the whole utterance

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Some examples

• object of commit is often something bad (foul,
  deception, offence)
• if something is described as rife, it is not good
  (crime, disease, mistakes), and describing it as
  rife expresses a negative connotation
  (speculation is rife)
• both the above exemplify unfavourable prosody,
  but other prosodies are possible
• good example claim vs admit responsibility for an
  atrocity

25
More power to your elbow

• Examples given in last few slides were largely
  subjective
• More interesting if we can back up observations
  with calculations of statistical significance
• Next time we will look at some simple statistical
  measures