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Corpus annotation and retrieval: an introduction. Paul Rayson Computing Department, Lancaster University Dawn Archer School of Humanities, University of Central Lancashire. Session outline. What is a corpus ? What is corpus linguistics ? Applying these techniques to historical data

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corpus annotation and retrieval an introduction

Corpus annotation and retrieval: an introduction

Paul Rayson

Computing Department, Lancaster University

Dawn Archer

School of Humanities, University of Central Lancashire

session outline
Session outline
  • What is a corpus?
  • What is corpus linguistics?
  • Applying these techniques to historical data
  • What research questions can we answer with CL techniques

… in linguistics …?

… in computing …?

… in history …?

Text Mining for Historians

July 17-18 2007 Glasgow University

1 background

1. Background

Corpora, corpus linguistics, annotation, retrieval methods

underlying assumption
Underlying assumption
  • Intuition is not enough to study language …
    • Reaction to Noam Chomsky’s focus on introspection in 1950s/60s
      • Empirical observation of naturally occurring data versus theory of how human language processing is actually undertaken

Text Mining for Historians

July 17-18 2007 Glasgow University

what is a corpus
What is a corpus?
  • Old meaning = “body of text” (Latin)
  • Now = (any) “collection of texts or language examples” – usually in an electronic format
  • Demonstrates extent to which CL-revival led by advances in computing technology

Text Mining for Historians

July 17-18 2007 Glasgow University

a corpus tends to be representative
A corpus tends to be “representative”

i.e. a balanced sample of a language or a particular variety of language --- c.f. national corpora (British, American, Czech, Polish …)

Reasoning?

  • Helps to remove intuitive bias
  • Helps us to find common/ rare phenomena

Exceptions …?

Text Mining for Historians

July 17-18 2007 Glasgow University

and large
And large …

… because size helps us to:

    • Establish norms about the variety being studied
    • Reveal lots of cases of rare features of language
  • Zipf’s law

Text Mining for Historians

July 17-18 2007 Glasgow University

size matters
Size matters!

Web

Present day

? billion

BNC

1990s

100 million

Brown/LOB

1960s

1 million

Text Mining for Historians

July 17-18 2007 Glasgow University

slide9

Web

Future

? billions

Collins Bank of English

Cambridge International Corpus

Oxford English Corpus

2006

600 million – 1 billion

Birmingham corpus

1980

10 million

Text Mining for Historians

July 17-18 2007 Glasgow University

so what is corpus linguistics
So what is corpus linguistics?

= the “study of language using corpora”

= empirical methodology

= a useful means of exploring:

  • Synchronic and diachronic variation
  • Syntax, semantics, pragmatics
  • Lexicography
  • Dialects, minority languages
  • Not just English

Text Mining for Historians

July 17-18 2007 Glasgow University

corpus techniques we utilise
Retrieval

Frequency profiling

Concordancing

Collocations

Key words

Key domains

Annotation

POS tagging

Semantic tagging

Corpus techniques we utilise

Text Mining for Historians

July 17-18 2007 Glasgow University

slide12

Annotation

    • Part of speechtagging
    • Semantic field tagging
  • Retrieval
    • Frequency lists
    • Concordances

Text Mining for Historians

July 17-18 2007 Glasgow University

key words

Keywords

Key words

What are “key words”?

And why are they so useful?

Text or reference corpus

Text

Text Mining for Historians

July 17-18 2007 Glasgow University

slide14

Key words

If we compare text A

… with text B

… we can discover the most significant items within text A

… and not only the frequent items

Word Clouds

Text Mining for Historians

July 17-18 2007 Glasgow University

collocations
Collocations
  • Collocation = a relationship between words that tend to occur together in texts
    • Words that tend to occur near word X are the collocates of word X (consider “fish and XXXXX”)
    • Based on frequency (how frequent separate vs. how frequent together)
  • The company a word keeps: implicit associations or assumptions
    • Bachelor: eligible, flat, life, days
    • Spinster: elderly, widows, sisters, parish

Text Mining for Historians

July 17-18 2007 Glasgow University

corpus software
Corpus software

Text Mining for Historians

July 17-18 2007 Glasgow University

modern methods in an historical setting focussing on emode period
Modern methods in an historical setting (focussing on EmodE period)
  • Tools/methods don’t take account of spelling variation
    • Variant spelling detector (VARD)
  • The need to use historically valid taxonomies or thesauri, or revise our existing modern tagsets
    • Historical Thesaurus of English
    • Spevack (1993)

Text Mining for Historians

July 17-18 2007 Glasgow University

using automated systems of annotation on historical texts is problematic
Using automated systems of annotation on historical texts is problematic …

EModE texts pose the following “problems”:

  • Archaic –eth and –(e)st verb suffixes, e.g. doth, hath, hast, sayeth, etc., which persist in specialised contexts: religious and poetic usage
  • Fused forms, e.g. ’Tis (It is)
  • Spellings that are variable even in modern-day usage, e.g. center/centre, skilful/skillful/skilfull, the suffixes -or/-our, -ise/-ize
  • Archaic forms like howbeit, betwixt, for which no obvious modern equivalent exists
  • Compound words, e.g. it self, now adays, in stead
  • Proper names of Latin origin that are sometimes modernised, e.g. Galilaeo (Galileo)
  • Due to different conventions and compositing practices

Text Mining for Historians

July 17-18 2007 Glasgow University

previous work in
Fuzzy search engine

Aimed at successful retrieval for novice users without expertise in the text

Expand the search term using known letter replacements

Changing dictionary built in to corpus annotation software

Back-dating inbuilt dictionaries by adding historical variants

Previous work in …

Corpus linguistics

Information Retrieval

Natural language processing

Text Mining for Historians

July 17-18 2007 Glasgow University

our scenario
Our scenario

POSTAGGER

SEMTAGGER

VARD:

Detect variant spellings and insert modern equivalents

Text Mining for Historians

July 17-18 2007 Glasgow University

an important point about the vard
An important point about the VARD

Although the VARD allows for the detection and “normalisation” of variants to their modern equivalents, it should be noted that ...

  • The original variants are retained in the text
  • We’re not carrying out spell checking per se (no “correct” spelling in EmodE period) ...
  • Our ultimate aim is to develop a system that automatically regularises variants within a text to their modernised forms so that historical corpora become more amenable to further annotation and analysis.

Text Mining for Historians

July 17-18 2007 Glasgow University

existing corpora
Existing corpora
  • What is already available:
    • LOB-family, Brown family (20th Century)
      • 15 genres: press, religion, skills & hobbies, biography, learned, fiction (detective, science, adventure), romance, humour
    • Lampeter (1640-1740)
      • Religion, Politics, Economy, Science, Law and Misc.
    • Corpus of English Dialogues (1560-1760)
      • Trial proceedings, depositions, drama, prose fiction
    • Helsinki (Old, Middle and Early Modern English)
    • Archer (1650-1990, sampled at 50 year periods)
      • Journals, letters, fiction, news, medicine, science

Text Mining for Historians

July 17-18 2007 Glasgow University

slide24

Other historical texts – not complied for corpus linguistics

Book Search

Text Mining for Historians

July 17-18 2007 Glasgow University

slide25

Changing English Across the 20th Century: a corpus-based studyucrel.lancs.ac.uk/20thCenturyEnglish/ Leverhulme Trust (2005-7)

  • Project outputs
  • Compile a new corpus of British English called Lancaster1901
  • Enhance the encoding and annotation of Lancaster1901 and the three existing corpora (Lancaster1931, LOB and FLOB)
  • 10 conference presentations
  • 1 book chapter
  • 1 book
  • 2 journal articles
  • Background:
  • Recent observations of significant shifts having occurred among expressions of obligation/necessity in the period 1961-1991 e.g.
    • a decline of the central modals MUST and NEED
    • a spread of the semi-modals HAVE TO, NEED TO
  • Research questions
  • Are these changes recent
  • How do these changes compare to the development of the semantic field of OBLIGATION/ NECESSITY as a whole?

Text Mining for Historians

July 17-18 2007 Glasgow University

application 2 historical cl
In particular, courtroom research (1640+), from a linguistic perspective

Utilise a specially designed corpus – Sociopragmatic Corpus – which has been annotated for:

age, gender, status and role.

speech acts such as questions, requests and commands

Application 2: Historical CL

<P 37>

[$ (^Record.^) $] <u stfunc="fol-ini" force="q" q="qy" qtype="d" qform="dec" speaker="s" spid="s4tgiles001" spsex="m" sprole1="re" spstatus="1" spage="8" addressee="s" adid="s4tgiles027" adsex="f" adrole1="w" adstatus="5" adage="x">He did not go out of your Company at all? </u>

[$ (^Ann.^) $] <u stfunc=“res" force=“h" a=“ca“ a2=“ela“ speaker="s" spid="s4tgiles027" spsex=“f“ sprole1=“w“ spstatus=“5" spage="8“ addressee="s“ adid="s4tgiles001“ adsex=“m" adrole1=“m" adstatus=“1“ adage="x">Yes about Ten a Clock.</u>

[$ (^Record.^) $] <u stfunc="fol" force="h" speaker="s“spid="s4tgiles001" spsex="m" sprole1="re" spstatus="1" spage="8" addressee="s" adid="s4tgiles027" adsex="f" adrole1="w" adstatus="5" adage="x">Woman you must be mistaken, he came to Town at Twelve or One, and might be in thy company, but it is plain he went to a Brokers in (^Long-lane^) , and so to the (^Artillery-Ground^) at (^Cripple-Gate^) , for I guess it might be so: Then they went to (^Whetstones-Park^) , and spent Six-Pence, and after that they went into (^Drury-lane^).</u>

[$ (^Giles,^) $] <u stfunc="rep" force="h" speaker="s" spid="s4tgiles005" spsex="m" sprole1="d" spstatus="1" spage="x" addressee="s" adid="s4tgiles001" adsex="m" adrole1="re" adstatus="1" adage="8">My Lord, she don\'t say she was with us all the while, but we came to an House where she was, and several other People our Neighbours. </u>

Text Mining for Historians

July 17-18 2007 Glasgow University

some important findings
Some important findings
  • Historical courtroom discourse is not just made up of questions and answers (even during examination sequences)
  • The frequency with which questions – and directives - were used, the function that they served, and their ability to achieve their social and/or interactional goal depended (in large part) on a number of socio-pragmatic factors:

type and date of trial position in discourse

role of user & addressee ultimate aim of interaction

  • 1640-1760 was a period of emerging and changing roles
  • Now beginning to explore the nineteenth century, i.e. period in which the courtroom adopted advocacy in its modern form (Cairns 1998)
    • Utilising full trials: emerging need to consider opening/closing statements

Text Mining for Historians

July 17-18 2007 Glasgow University

historical text mining htm
Historical text mining (HTM)

Historical theory

HTM

Natural language processing & Computational linguistics

Corpus Linguistics

Linguistic theory

Corpus

Empirical evidence to inform theory

Statistical and rule-based language models

Text Mining for Historians

July 17-18 2007 Glasgow University

what research questions would you like to answer but can t
What research questions would you like to answer, but can’t?
  • Search engines for new text collections and digital libraries
  • Named entity extraction for GIS
  • Variant spellings
  • Historical text mining
  • New research methods in History

Text Mining for Historians

July 17-18 2007 Glasgow University

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