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Università Degli Studi Di Macerata UNIVERSITÀ DEGLI STUDI DI MACERATA Dipartimento di Studi Umanistici – Lingue, Mediazione, Storia, Lettere, Filosofia Corso di Laurea Magistrale in Lingue Moderne per la Comunicazione e la Cooperazione Internazionale (ClasseLM-38) Traduzione per laComunicazione Internazionale – inglese -mod. B STRUMENTI E TECNOLOGIE PER LA TRADUZIONESPECIALISTICA 1 What is a corpus? Some (authoritative) definitions • “a collection of naturally-occurring language text, chosen to characterize a state or variety of a language” (Sinclair, 1991:171) • “a collection of texts assumed to be representative of a given language, dialect, or other subset of a language, to be used for linguistic analysis” (Francis, 1992:7) • “a closed set of texts in machine-readable form established for general or specific purposes by previously defined criteria” (Engwall, 1992:167) • “a finite-sized body of machine-readable text, sampled in order to be maximally representative of the language variety under consideration” (McEnery & Wilson, 1996:23) • “a collection of (1) machine-readable (2) authentic texts […] which is (3) sampled to be (4) representative of a particular language or language variety” (McEnery et al., 2006:5) What is / is not a corpus…? • A newspaper archive on CD-ROM? The answer is • An online glossary? always “NO” • A digital library (e.g. Project (see Gutenberg)? definition) • All RAI 1 programmes (e.g. for spoken TV language) Corpora vs. web •Corpora: – Usually stable •searches can be replicated – Control over contents •we can select the texts to be included, or have control over selection strategies – Ad-hoc linguistically-aware software to investigate them •concordancers can sort / organise concordance lines • Web (as accessed via Google or other search engines): – Very unstable •results can change at any time for reasons beyond our control – No control over contents •what/how many texts are indexed by Google’s robots? – Limited control over search results •cannot sort or organise hits meaningfully; they are presented randomly Click here for another corpus vs. Google comparison What types of corpora exist? A brief overview • A corpus is a principled collection of naturally occurring electronic texts designed to be a representative sample of language in actual use • Some of the main features and criteria used to describe and classify corpora: general closed / finite specialised open-ended (monitor) written raw (pre-corpus) spoken (transcribed) marked-up (augmented) multimodal (audio/video) POS-tagged (augmented) balanced (sample) annotated (augmented) opportunistic monolingual synchronic bi- / multilingual diachronic parallel static comparable dynamic An example of planned balance: the British National Corpus • 100 m words of contemporary spoken and written British English • Representative of British English “as a whole” • Designed to be appropriate for a variety of uses: lexicography, education, research, commercial applications (computational tools) • Balanced with regard to genre, subject matter and style • Sampling and representativeness very difficult to ensure BNC • 4,124 texts: 90% written, 10% spoken • Largest collection of spoken English ever collected (10m words), but reflects typical imbalance in favour of written text (for understandable practical reasons) • Written portion: 75% informative, 25% imaginative BNC written material Sources: • 60% books • 25% periodicals • 5% brochures and other ephemera • E.g. bus tickets, produce containers, junk mail • 5% unpublished letters, essays, minutes • 5% plays, speeches (written to be spoken) Register levels: • 30% literary or technical “high” • 45% “middle” • 25% informal “low” BNC Subject coverage • Planned to reflect pattern of book publishing in UK over last 20 years Subject Number of texts % of total written Imaginative 625 22 World affairs 453 18 Social science 510 15 Leisure 374 11 Applied science 364 8 Commerce 284 8 Arts 259 8 Natural science 144 4 Belief & thought 146 3 Unclassified 50 3 BNC Spoken corpus • Context-governed material • Lectures, tutorials, classrooms • News reports • Product demonstrations, consultations, interviews • Sermons, political speeches, public meetings, parliamentary debates • Sports commentaries, phone-ins, chat shows • Samples from 12 different regions 10/18 BNC Spoken corpus • Ordinary conversation • 2000 hrs from 124 volunteers, 38 different regions • Four different socio-economic groupings • Equal male and female, age range 15 to 60+ • All conversations over a 2-day period recorded • No secret recording, and allowed to erase • Systematic details kept of time, location, details of participants (sex, age, race, occupation, education, social group, ), topic, etc. • Transcription issues: • include false starts, hesitations, etc. • some paralinguistic features (shouting, whispering), • use of dialect words/grammar • but no phonetic information What types of corpora exist? A brief overview • A corpus is a principled collection of naturally occurring electronic texts designed to be a representative sample of language in actual use • Some of the main features and criteria used to describe and classify corpora: general closed / finite specialised open-ended (monitor) written raw (pre-corpus) spoken (transcribed) marked-up (augmented) multimodal (audio/video) POS-tagged (augmented) balanced (sample) annotated (augmented) opportunistic monolingual synchronic bi- / multilingual diachronic parallel static comparable dynamic 12 Dynamic (Monitor) vs static (Finite) • A static corpus will give a snapshot of language use at a given time • Easier to control balance of content • May limit usefulness, esp. as time passes • A dynamic corpus is ever-changing • Called “monitor” corpus because allows us to monitor language change over time Key concepts and technical notions in corpus-based translation studies • Wordlist, frequency list, keyword list • Types, tokens, type/token ratio (lexical variation) • Function/grammatical words vs. content/lexical words (lexical density) “Type” and “token” • “Token” means individual occurrence of a word • “Type” means instance of a given word • The man saw the girl with the telescope • 8 tokens, 6 types • “Type” may refer to lexeme, or individual word form • run, runs, ran, running: 1 or 4 types? Key concepts and technical notions • Wordlist, frequency list, keyword list • Types, tokens, type/token ratio (lexical variation) • Function/grammatical words vs. content/lexical words (lexical density) • Concordance (concordancing software) • KWIC (keyword in context) • Nodeword • Sorting Concordance for nodeword “eyes” (sorted 1L) generated from the BNC Key concepts and technical notions • Wordlist, frequency list, keyword list • Types, tokens, type/token ratio (lexical variation) • Function/grammatical words vs. content/lexical words (lexical density) • Concordance (concordancing software) • KWIC (keyword in context) • Nodeword • Sorting • Collocation (collocates) • Lemmatisation (morphological analysis) • (POS-)Tagging (grammatical analysis) • Parsing (syntactic analysis) www.nature.com/nature/journal/v455/n7215/full/455835b.html20 General / reference monolingual corpora (of English) Last week, tens of thousands of researchers took to the streets to register their opposition to a proposed bill designed to control civil- service spending. Took to the streets • http://corpus.leeds.ac.uk/internet.html • English • Let’s try to understand: • Meaning • Extended (sentential) co-text, preferential co-selections • Context(s) of use • Semantic preference • Semantic prosody Using general / reference monolingual corpora (from/on the Web): Leeds Internet corpora * http://corpus.leeds.ac.uk/internet.html Let’s explore internal variation - Examples of (possible) useful queries • Any other forms of the verb take? (colligational constraints) • Plural/singular of the noun street? (colligational constraints) • Other verbs? (collocational flexibility) • Other nouns? (collocational flexibility) • Select “CQP syntax only” * (automatic POS-tagging!) • http://cwb.sourceforge.net/files/CQP_Tutorial/ • Look at the examples on the following slides for guidance and adapt those models to your searches • Try out a number of different options to familiarise yourself with the search syntax, and understand what kinds of searches it can support Now the translation into Italian of “took to the streets” • Verb? • Preposition? • andare • in • scendere • nella/nelle • …? • per la/per le? • …? • Noun? • strada/strade • piazza/piazze Which queries do we • …? need? How many are necessary? Last week, tens of thousands of researchers took to the streets to register their opposition to a proposed bill designed to control civil- service spending. REGISTER ONE’S OPPOSITION • Now search the BNC for this expression. • What does it mean? • Which “feelings” are usually “registered”? • interest • concern • support • dismay • frustrations • dissatisfaction • disapproval • protest • commitment • … Monolingual general / reference corpora available online (at least partially, i.e. as demos) • British National Corpus (BNC, British English) • www.natcorp.ox.ac.uk • COCA (American English) • http://corpus.byu.edu/coca/ • The CORIS corpus (Italian) • http://corpora.dslo.unibo.it/coris_ita.html • Leeds Internet corpora • English, Chinese, Arabic, French, German, Italian, Japanese, Polish, Portuguese, Russian, Spanish: http://corpus.leeds.ac.uk/internet.html • Mannheim corpora (German) • http://corpora.ids-mannheim.de/ccdb explore the Web • Corpus del Español (Spanish) to see what other • www.corpusdelespanol.org
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