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What is a CORPUS?

• A corpus is a collection of pieces of language
  that are selected and ordered according to
  explicit linguistic criteria in order to be used
  as a sample of the language
• (Sinclair 1996)

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What is a CORPUS?

• the term corpus as used in modern
  linguistics can best be defined as a collection
  of sampled texts, written or spoken, in
  machine-readable form which may be annotated with
  various forms of linguistic information
• (McEnery, Xiao and Tono 2006)

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Key concepts re. Corpora

• Machine-readable texts
• Authentic texts
• Sampled texts
• Representative of a particularlanguage or
  language variety

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Is Corpus Linguistics a new approach to the study
of language?

• The expression Corpus Linguistics first appeared
  in the early 80s.
• Corpus-based language study,however has a
  substantial history.

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Corpus-based language study

• In the pre-Chomskyan era
• Field linguists (Boas)
• Structuralists (Sapir, Newman, Bloomfield, Pike,
  etc.)
• Corpora where few paper slips with data.
  Shoebox Corpora Non-representative.
• Corpus-based only in that the methodology was
  empirical and based on observable data.

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The 50s the protests

• Chomsky (1962) accused the (contemporary) corpus
  methodology, by reason of the skewedness of
  corpora.
• Non-representative, time consuming, competence
  vs. performance, I-language vs. E-language
• Corpora were marginalized.

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The revolutionary 60s

• With the advances in computer technology the
  exploitation of massive corpora became feasible.
• Brown Corpus
• Brown University Standard Corpus of American
  Present-day English

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The 80s the boom

• From the 80s onwards the number and size of
  corpora and corpus based studies have increased
  dramatically.
• Corpora have revolutionized almost all
  branches of linguistics.

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A few remarks

• Computers
• allow us to speed up the processing of data.
• avoid human bias in data analysis
• allow the enrichment of data with metadata

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Intuition vs. Corpus

• Intuition should be applied with caution
• Influence of dialect, sociolect, idiolect
• No universal agreement on (degree of)
  acceptability
• Informants monitor their use of language
  (non-spontaneous)
• Introspection is not observable

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Intuition vs. Corpus

• Corpus-based approach draws upon authentic or
  real texts
• Computer-based analysis can retrieve differences
  that intuition alone cannot perceive
• Reliable quantitative data

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Should we dismiss intuition then?

• Not at all!
• The key to using corpus data is to find the
  balance between the use of corpus data and the
  use of ones own intuition.

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Should we dismiss intuition then?

• Not all research questions can be addressed by
  the corpus-based approach.
• Corpus-based approach and intuition-based
  approach
• ARE NOT MUTUALLY EXCLUSIVE

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Leech (199114) writes

• Neither the corpus linguist of the 1950s,
  who rejected intuition, nor the general linguist
  of the 1960s, who rejected corpus data, was able
  to achieve the interaction of data coverage and
  the insight that characterise the many successful
  corpus analyses of recent years.

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Is CL a methodology or a theory?

• No universal agreement.
• CL is a METHODOLOGY and not an independent branch
  of linguistics such as semantics, pragmatics,
  syntax, etc.
• CL can be employed to explore almost any area of
  linguistic research.

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Corpus-based or Corpus-driven approaches?

• Corpus-based approaches are used to expound,
  test or exemplify theories and descriptions that
  were formulated before large corpora became
  available to inform language study
  (Tognini-Bonelli 200165).
• Therefore, corpus-based linguists are not
  strictly committed to corpus data and they would
  discard inconvenient evidence by insulation,
  standardisation and instantiation (i.e. via
  corpus annotation).

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Corpus-based or Corpus-driven approaches?

• Corpus-driven linguists are strictly committed
  to the integrity of the data as a whole.
• Theoretical statements are fully consistent with,
  and reflect directly, the evidence provided by
  the corpus.
• (Tognini-Bonelli 200184-85).

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Corpus-based or Corpus-driven approaches?

• The distinction is overstated, they are 2
  idealized extremes.
• 4 basic differences among the 2 approaches
• Types of corpora used
• Attitudes towards theories and intuitions
• Focuses of research
• Paradigmatic claims

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• C.B. Approaches
• Corpus must be representative and balanced
• Size is not all-important
• Minimum frequency is used to exclude non-relevant
  results
• In favour of corpus annotation CB approaches
  generally have existing theory as a starting
  point and correct and revise such theory in the
  light of corpus evidence
• Distinction between the different levels of
  language analysis.

• C.D. Approaches
• Corpus will balance itself when it grows to be
  big enough (cumulative representativeness)
• Corpus must be very large
• Corpus evidence is exploited fully, but this way
  the number of the combinations is enormous
• Against corpus annotation (no preconceived
  theories)
• No distinction betweenlexis, syntax,
  pragmatics,etc. There is only 1 levelof
  language descriptionthe functionally complete
  unit of meaning or languagepatterning

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• We will only refer to
• CORPUS-BASED APPROACHES
• A few key notions in
• Corpus Linguistics

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Representativeness

• Essential feature of a corpus.
• Balance (the range of genres included in a
  corpus) and sampling (how the text chunks for
  each genre are selected) ensure
  representativeness.

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Representativeness

• A corpus is representative if
• the findings based on its contents cane be
  generalized to the said language variety (Leech
  1991)
• its samples include the full range of
  variability in a population (Biber 1993)

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Representativeness

• It changes over time (Hunston 2002) if a corpus
  is not regularly updated, it rapidly becomes
  unrepresentative.

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Representativeness

• Criteria to select texts for a corpus
• External criteria (Bibers situational
  perspective) defined situationally, e.g. genres,
  registers, text types, etc.
• Internal criteria (Bibers linguistic
  perspective) defined linguistically, taking into
  account the distribution of linguistic features.
  CIRCULAR because a corpus is typically design
  to study linguistic distribution, so there is no
  point in analysing a corpus where distribution of
  linguistic features is predetermined.

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Representativeness

• 2 main types (for the range of text categories
  represented)
• General corpora a basis for an overall
  description of a language (variety) their r.
  depends on the sampling from a broad range of
  genres.
• Specialized corpora domain- or genre specific
  corpora their r. can be measured by the degree
  of closure or saturation (lexical features).

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Balance

• The range of text categories included in the
  corpus
• The acceptable b. is determined by the intended
  uses.
• A balanced corpus covers a wide range of text
  categories which are supposed to be
  representative of the language (variety) under
  consideration.

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Balance

• There is no scientific measure for balance.
• It is more important for sample corpora than
  for monitor corpora

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Sampling

• A corpus is a sample of a given population
• A sample is representative if what we find for
  the sample holds for the general population
• Samples are scaled-down versions of a larger
  population

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Sampling

• Sampling unit for written text, a s.u. could be
  a book, periodical or newspaper.
• Population the assembly of all sampling units
  it can be defined in terms of language
  production, reception (demographic, sex, age,
  etc.) or language as a product (category, genre
  of language data).
• Sampling frame the list of sampling units

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Sampling

• Sampling techniques
• Simple random sampling all sampling units within
  the sampling frame are numbered and the sample is
  chosen by use of a table or random numbers rare
  features could not be accounted for.
• Stratified random sampling the population is
  divided in relatively homogeneous groups, i.e.
  the strata, and then these latter are sampled at
  random never less representative than the former
  method.

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Sampling

• Sample size
• Full texts no balance peculiarity of
  individual texts may show through.
• Text chunks are sufficient (e.g. 2000 running
  words) frequent linguistic features are stable
  in their distribution and hence short text chunks
  are sufficient for their study (Biber 1993). Text
  initial, middle and end samples must be balanced.

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Sampling

• Proportion and number of samples
• The number of samples across text categories
  should be proportional to their frequencies
  and/or weights in the target population in order
  for the resulting corpus to be considered as
  representative

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What matters is the Research Question!

• Claims of corpus representativeness and balance
  should be interpreted in relative terms as there
  is no objective way to balance a corpus or to
  measure its representativeness.
• Representativeness is a fluid concept the
  research question that one has in mind when
  building a corpus determines what is an
  acceptable balance for the corpus one should use
  and whether it is suitably representative.

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Data collection

• Spoken data must be transcribed from audio
  recordings.
• Written text must be rendered machine-readable by
  keyboarding or OCR (Optical Character
  Recognition) scanning.
• Language data so collected form a RAW CORPUS.

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Corpus Mark-up

• System of standard codes inserted into a
  document stored in electronic form to provide
  information about the text itself and govern
  formatting, printing and other processes.
• Most widely used mark-up schemes
• TEI (Text Encoding Initiative)
• CES (Corpus Encoding Standard)

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Corpus Mark-up

• It is essential in corpus-building because
• sampled texts are out of context and it allows
  to recover contextual information
• it provides more information than the file
  names alone (re. text types, sociolinguistic
  variables, textual information structure)
• it ads value to the corpus because it allows for
  a broader range of questions to be addressed
• it allows to insert editorial comments during
  the corpus building process.

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Corpus Mark-up

• Extra-textual and textual information must be
  kept separate from the corpus data.
• Examples
• COCOA mark-up scheme
• ltA WILLIAM SHAKESPEAREgt
• A author, attribute name
• WILLIAM SHAKESPEARE attribute value

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TEI Mark-up Scheme

• Each individual text is a document consisting in
  a header and a body, in turn composed of
  different elements.
• Ex. in the header there are 4 main elements
• A file description ltfileDescgt
• An encoding description ltencodingDescgt
• A text profile ltprofileDescgt
• A revision history ltrevisionDescgt
• Tags can be nested, i.e. they can appear inside
  other elements.

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TEI Mark-up Scheme

• It can be expressed using a number of different
  formal languages.
• SGML (Standard GeneralizedMark-up Language
  used bythe BNC)
• XML (Extensible Mark-up Language)

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CES Mark-up Scheme

• Designed specifically for the encoding of
  language corpora.
• Document-wide mark-up (bibliographical
  descripion, encoding description, etc.)
• Gross structural mark-up (volume, chapter,
  paragraph, footnotes, etc. specifies recommended
  character sets)
• Mark-up for subparagraph structures (sentence,
  quotations, words, abbreviations, etc.)

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CES Mark-up Scheme

• It specifies a minimal encoding level that
  corpora must achieve to be considered
  standardized in terms of descriptive
  representation as well as general architecture.
• 3 levels of standardization designedto achieve
  the goal of universal document interchange
• Metalanguage level
• Syntactic level
• Semantic level

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Corpus Annotation

• Necessary in order to extract relevant
  information from corpora.
• The process of adding interpretive,
  linguistic information to an electronic corpus of
  spoken and/or written language data
• (Leech 1997)

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Annotation vs. Mark-up

• Corpus mark-up provides objective, verifiable
  information.
• Annotation is concerned withinterpretive
  linguistic information.

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The advantages of annotation

1. It makes extracting information easier, faster
   and enables human analysts to exploit and
   retrieve analyses of which they are not
   themselves capable.

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The advantages of annotation

• 2. Annotated corpora are reusable resources.
• 3. Annotated corpora are multifunctional they
  can be annotated with a purpose and be reused
  with another.

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The advantages of annotation

• 4. Corpus annotation records a linguistic
  analysis explicitly.
• 5. Corpus annotation provides a standard
  reference resource, a stable base of linguistic
  analyses, so that successive studies can be
  compared and contrasted on a common basis.

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Criticisms to corpus annotation

1. Annotation produces cluttered corpora
2. Annotation imposes an analysis
3. Annotation overvalues corpora making them less
   accessible
4. Is annotation accurate and consistent?

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How are corpora annotated?

• Automatic annotation
• Computer-assisted annotation
• Manual annotation
• Sinclair (1992) the introduction of the human
  element in corpus annotation reduces consistency.

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Types of annotation

• Different types of annotation can be carried out
  with different means.
• For some types automatic annotation is very
  accurate. Other types require post-editing,
  i.e. human correction.

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Types of annotation

• Corpora can be annotated at different levels of
  linguistic analysis.
• Phonological level
• Syllable boundaries (phonetic/phonemic
  annotation)
• Prosodic features (prosodic annotation)

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Types of annotation

• Morphological level
• Prefixes
• Suffixes
• Stems
• (morphological annotation)

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Types of annotation

• Lexical level
• Part of speech (POS Tagging)
• Lemmas (lemmatization)
• Semantic fields (semantic annotation)
• Syntactic level
• parsing
• treebanking
• bracketing

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Types of annotation

• Discourse level
• Anaphoric relations (coreference annotation)
• Speech acts (pragmatic annotation)
• Stylistic features such as speech and thought in
  presentation (stylistic annotation).

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POS Tagging

• POS is the most common type of annotation.
• Also known as grammatical tagging or
  morpho-syntactic annotation.
• It provides the basis of further forms of
  analysis such as parsing and semantic
  annotation.
• Many linguistic analyses, e.g. the collocates of
  a word depend heavily on POS tagging.

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POS Tagging

• It can be performed automatically with taggers
  like CLAWS
• http//www.comp.lancs.ac.uk/ucrel/claws/
• You can try it for free online.
• Examples of tags NN1 (noun), VVZ (verb in the
  third person of the simple present tense), VVD
  (verb in the simple past form), ADJ0 (adjective
  in the basic form), etc.

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POS Tagging

• Problems
• Word segmentation (tokenization)
• Multiwords (so that, inspite of)
• Mergers (cant, gonna)
• Variably spelled compounds (noticeboard,
  notice-board, notice board)

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Lemmatization

• Type of annotation that reduces the inflextional
  variants of words to their respective lexemes or
  lemmas as they appear in dictionary entries
• Do, does, did, done, doing DO
• Corpus, corpora CORPUS
• Small capital letters are the convention.

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Lemmatization

• It is important in vocabulary studies and
  lexicography, e.g. in studying the distribution
  pattern of lexemes and improving dictionaries
  and computer lexicons.
• It can be automatically performed.

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Parsing

• Once a corpus is POS tagged, it is possible to
  bring these morpho-syntactic categories into
  higher level syntactic relationships with one
  another, that is, to analyse the sentences in a
  corpus into their constituents.
• Parsing consists in bracketing.
• It can be automated but with a low precision
  rate.

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Parsing

• Example
• (S (NP Mary)
• (VP visited)
• (NP a
• (ADJP very nice)
• boy)))

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Semantic annotation

• It assigns codes indicating the semantic features
  of the semantic fields of the words in a text. It
  is knowledge-based so it needs to be manual most
  of the time.
• Two types
• One marks the semantic relationships between the
  constituents in a sentence
• One marks the semantic features of words in a
  text

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Coreference annotation

• Pronouns
• Repetition
• Substitution
• Ellipsis
• Computer-assisted at best.

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Pragmatic annotation

• Speech/dialogue acts in domain-specific dialogue.
• The most coherent system is DRI (Discourse
  Representation Initiative).
• 3 layers of coding
• Segmentation (dividing dialogue in textual
  units, utterances)
• Functional annotation (dialogue act annotation)
• Utterance tags (applying utterance tags that
  characterize the role of the utterance as a
  dialogue act)

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Pragmatic annotation

• Utterance tags
• Communicative status (intelligible, complete,
  etc.)
• Information level and status (indicating the
  semantic content of the utterance and how it
  relates to the task in question)
• Forward-looking communicative function
  (utterances that may constrain or affect the
  discourse, e.g. assert, request, question and
  offer)
• Backwarding-looking communicative function
  (utterances that relate to previous parts of the
  discourse, e.g. accept, backchannelling, answer)

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Stylistic annotation

• It is particularly associated with stylistic
  features in literary texts.
• An example the representation of peoples
  speech and thoughts, known as speech ad thought
  presentation (STP)

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Other types of tagging

• Error tagging
• Problem-oriented annotation

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Types of corpora

• Multilingual
• Monolingual

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Multilingual Corpora

• Parallel corpora (source texts plus
  translations) Canadian Hansard
• Comparable corpora (monolingual subcorpora
  designed using the same sampling techniques)
  Aahrus corpus of contract law
• Multilingual
• Bilingual

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Multilingual Corpora

• Important resources for translation and
  contrastive studies.
• Multilingual corpora
• give new insight into the language compared
• can be used to study language specific and
  universal features
• illuminate differences between source texts and
  translations
• can be used for a number of practical
  applications, in lexicography, language teaching,
  translation, etc.

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Parallel Corpora

• Bilingual vs.Multilingual
• Unidirectional (from La to Lb or from Lb to Lc
  alone) vs. Bidirectional (from La to Lb and from
  Lb to La) vs. Multidirectional (from La to Lb, Lc
  etc.)

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Comparable corpora

• A corpus containing components that are collected
  using the same sampling techniques and similar
  balance and representativeness, e.g. the same
  proportions of the texts of the same genres in
  the same domains in a range of different
  languages in the same sampling period.

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Comparable vs. parallel corpora

• The sampling frame is essential for comparable
  corpora but not for parallel corpora because the
  texts are exact translations of each other.

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Corpus Alignment

• In order for us to be able to fully exploit
  parallel corpora, they need to be aligned.
• Different types of alignment
• Word-level alignment
• Sentence-level alignment
• Paragraph alignment

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General Corpora

• British National Corpus (100,106,008 words)
• The American National Corpus
• ICE-CUP

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Specialized Corpora

• Guangzhou Petroleum English Corpus (411,612 words
  of written English from the petrochemical domain)
• HKUST Computer Science Corpus (1,000,000 words of
  written English sampled from undergraduate
  textbooks in computer science.
• CPSA (Corpus of Professional Spoken American
  English)
• MICASE (1,700,000 words of English spoken in the
  academic domain)

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Written Corpora

• BROWN Corpus (written texts, AE in 1961)
• LOB Corpus (Comparable to BROWN Corpus, BE, early
  1960s)
• FROWN Corpus (AE, Early 1990s)
• FLOB Corpus (BE, Early 1990s)

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Spoken Corpora

• London-Lund Corpus (LLC)
• Lancaster/IBM Spoken English Corpus (SEC)
• Cambridge and Nottingham Corpus of Discourse in
  English (CANCODE)
• Santa Barbara Corpus of Spoken American English
  (SBCSAE)
• Wellington Corpus of Spoken New Zealand English
  (WSC)

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Synchronic Corpora

• Useful to compare varieties of English. Texts
  date all to the same period.
• Brown and Lob
• Frown and Flob
• International Corpus of English (ICE) (Texts
  produced after 1989)
• BNC

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Diachronic Corpora

• Texts date to different periods in time. Ideal to
  study language change and history.
• Brown/Frown
• Lob/Flob
• Helsinki Diachronic Corpus of English Texts
  (8th-18th century)
• Archer Corpus A representative Corpus of
  Historical English Registers (BE and AE,
  1650-1990).

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Learner/developmental Corpora

• Lstr or L2 acquisition/L1 acquired by children
• CHILDES (DC)
• International Corpus of Learner English ICLE
  (LC)
• Cambridge Learner Corpus (LC)

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Monitor Corpora

• Constantly supplemented with fresh material and
  keep increasing in size, though the proportion of
  text types included in the corpus remains
  constant.
• Bank of English (BoE)
• Global English Monitor Corpus
• AVIATOR

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The BNC

• The British National Corpus (BNC) is a 100
  million word collection of samples of written and
  spoken language from a wide range of sources,
  designed to represent a wide cross-section of
  current British English, both spoken and written.

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The BNC

• The written part of the BNC (90) includes, for
  example, extracts from regional and national
  newspapers, specialist periodicals and journals
  for all ages and interests, academic books and
  popular fiction, published and unpublished
  letters and memoranda, school and university
  essays, etc. The spoken part (10) includes a
  large amount of unscripted informal conversation,
  recorded by volunteers selected from different
  age, region and social classes in a
  demographically balanced way, together with
  spoken language collected in all kinds of
  different contexts, ranging from formal business
  or government meetings to radio shows and
  phone-ins.

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The BNC

• The corpus is encoded according to the
  Guidelines of the Text Encoding Initiative (TEI)
  to represent both the output from CLAWS
  (automatic part-of-speech tagger) and a variety
  of other structural properties of texts (e.g.
  headings, paragraphs, lists etc.). Full
  classification, contextual and bibliographic
  information is also included with each text in
  the form of a TEI-conformant header.

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What sort of corpus is the BNC?

• Monolingual It deals with modern British
  English, not other languges used in Britain.
  However non-British English and foreign language
  words do occur in the corpus.
• Synchronic It covers British English of the late
  twentieth century, rather than the historical
  development which produced it.
• General It includes many different styles and
  varieties, and is not limited to any particular
  subject field, genre or register. In particular,
  it contains examples of both spoken and written
  language.
• Sample For written sources, samples of 45,000
  words are taken from various parts of
  single-author texts. Shorter texts up to a
  maximum of 45,000 words, or multi-author texts
  such as magazines and newspapers, are included in
  full. Sampling allows for a wider coverage of
  texts within the 100 million limit, and avoids
  over-representing idiosyncratic texts.

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BNC and Sketchengine

• Sketch Engine is an excellent user-interface to
  query the BNC.
• Here are some screenshots.

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An example of a POS-tagged text

• I've been giving some thought to the whole idea
  of writing a book as of late (I've also been
  giving some thought to winning the lottery, and
  we can all see where that's got me) and it came
  to me while showering the other night that if I
  were to ever write a book (which ain't gonna
  happen, but let's just say for the sake of
  argument) I would bill myself as the anti-Francis
  Mayes.

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An example of a POS-tagged text

• I_PNP 've_VHB been_VBN giving_VVG some_DT0
  thought_NN1 to_PRP the_AT0 whole_AJ0 idea_NN1
  of_PRF writing_VVG a_AT0 book_NN1 as_PRP21
  of_PRP22 late_AJ0 (_( I_PNP 've_VHB also_AV0
  been_VBN giving_VVG some_DT0 thought_NN1 to_PRP
  winning_VVG the_AT0 lottery_NN1 ,_, and_CJC
  we_PNP can_VM0 all_DT0 see_VVI where_AVQ that_DT0
  's_VHZ got_VVN me_PNP )_) and_CJC it_PNP came_VVD
  to_PRP me_PNP while_CJS showering_VVG the_AT0
  other_AJ0 night_NN1 that_CJT if_CJS I_PNP
  were_VBD to_TO0 ever_AV0 write_VVI a_AT0 book_NN1
  (_( which_DTQ ai_UNC n't_XX0 gon_VVG na_TO0
  happen_VVI ,_, but_CJC let_VM021 's_VM022
  just_AV0 say_VVI for_PRP the_AT0 sake_NN1 of_PRF
  argument_NN1 )_) I_PNP would_VM0 bill_NN1
  myself_PNX as_PRP the_AT0 anti-Francis_AJ0
  Mayes_NP0 ._.