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 • 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 Corpora, corpus linguistics, annotation, retrieval methods

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? • 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” 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 … … 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! Web Present day ? billion BNC 1990s 100 million Brown/LOB 1960s 1 million Text Mining for Historians July 17-18 2007 Glasgow University

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? = 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

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

Annotation • Part of speechtagging • Semantic field tagging • Retrieval • Frequency lists • Concordances Text Mining for Historians July 17-18 2007 Glasgow University

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

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 • 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 Text Mining for Historians July 17-18 2007 Glasgow University

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 … 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

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 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 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

2. Historical data

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

Other historical texts – not complied for corpus linguistics Book Search Text Mining for Historians July 17-18 2007 Glasgow University

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

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 [$ (^Record.^) $] <u stfunc="fol-ini" force="q" q="qy" qtype="d" qform="dec" speaker="s" spid="s4tgiles001" spsex="m" sprole1="re" spstatus="1" spage="8" addressee="s" adid="s4tgiles027" adsex="f" adrole1="w" adstatus="5" adage="x">He did not go out of your Company at all? [$ (^Ann.^) $] Yes about Ten a Clock. [$ (^Record.^) $] Woman you must be mistaken, he came to Town at Twelve or One, and might be in thy company, but it is plain he went to a Brokers in (^Long-lane^) , and so to the (^Artillery-Ground^) at (^Cripple-Gate^) , for I guess it might be so: Then they went to (^Whetstones-Park^) , and spent Six-Pence, and after that they went into (^Drury-lane^). [$ (^Giles,^) $] 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. Text Mining for Historians July 17-18 2007 Glasgow University

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 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

3. Over to you …

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

Corpus annotation and retrieval: an introduction