Data capture and corpus markup Kron Data to be collected •Like other decisions in corpus creation (e.g. balance, representativeness, size), the kind of data to be collected also depends on your research questions –If you wish to compare British English and American English, you will need to collect spoken and / or written data produced by native speakers of the two regional varieties of English –If you are interested in how Chinese speakers acquire English as a second language, you will then need to collect the English data produced by Chinese learners to create a learner corpus –If you are interested in how the English language has evolved over centuries, you will need to collect samples of English produced in different historical periods to build a historical or diachronic corpus Data capture •Having developed an understanding of the type of data you need to collect, and having made sure that no ready-made corpus of such material exists, you’ll need to capture the data •Data digitalisation –Machine-readability is a de facto feature of a modern corpus Data capture •Text must be rendered machine-readable –Keyboarding –OCR (Optical Character Recognition) scanning –Transcribing audio/video recording •Existing electronic data is preferred over paper-based materials –The Web as an important source of machine-readable data for many languages –Converting other file format such as HTML, Word, PDF into plain text format •The World-Wide-Web (WWW) is an important source of electronic text archives Some useful data source •Oxford Text Archive –http://ota.ahds.ac.uk/ –Oldest text archive - thousands of texts (and many well-known corpora) in more than 25 different languages •Project Gutenberg –http://www.gutenberg.org/catalog/ –First producer of free electronic books – 2,8000 e-books! •Digital collections of university libraries e.g. –http://www.digitalcurationservices.org/digital-stewardship-services/etext-projects/ –http://onlinebooks.library.upenn.edu/ •Corpus4u electronic text archives –http://www.corpus4u.org/forumdisplay.php?f=21 Copyright in corpus creation •A corpus consisting entirely of copyright-free old texts is not useful in study of contemporary language •Copyright is a major issue in data collection if you are to publish or make your corpus publicly available •The samples taken under the convention of ‘fair dealing’ in copyright law are so small as to jeopardize any claim of balance or representativeness •There is as yet no satisfactory solution to the issue of copyright in corpus • Copyright in corpus creation •Tips for copyright issues –Usually easier to obtain permission for samples than for full texts –Easier for smaller samples than for larger ones –If you show that you are acting in good faith, and only small samples will be used in non-profit-making research, copyright holders are typically pleased to grant you permission –You don’t need to worry about copyright if you build a corpus for your private use! Corpus markup •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 –Describing the document (“metadata” like source, name, author, date, etc) –Marking boundaries for paragraphs, sentences, and words, omissions etc –Displaying markup (font, font size, positioning) Example of markup markup start tag end tag Why markup? •Markup recovers contextual information of sampled texts which are taken out of context •Markup allows for a broader range of research questions to be addressed by providing extra information such as text types, sociolinguistic variables, structural organization •Markup allows corpus builders to insert editorial comments during the corpus building process •Pre-processing written texts (e.g. tables and graphs), and particularly transcribing spoken data, also involves markup (e.g. pause, paralinguistic features etc) Markup schemes •The extra markup information must be kept separate from the textual data in a corpus •Markup schemes –COCOA –TEI (Text Encoding Initiative) –CES (Corpus Encoding Standard) COCOA reference •One of the earliest markup schemes •Consisting of a set of attribute names and values enclosed in angled brackets –e.g. •attribute name = A (author) •attribute value = WILLIAM SHAKESPEARE •Only encoding a limited set of features such as authors, titles and dates •Giving way to more modern schemes TEI guidelines •The Text encoding Initiative: sponsored by three major academic associations concerned with humanities computing –The Association for Computational Linguistics (ACL) –The Association for Literary and Linguistic Computing (ALLC) –The Association for Computers and the Humanities (ACH) •Aiming to facilitate data exchange by standardizing the markup or encoding of information stored in electronic form TEI guidelines •Each individual text is a document consisting in a header and a body, which are in turn composed of different elements •TEI corpus header has 4 principal elements –A file description (): a full bibliographic description –An encoding description (): relationship between an electronic text and its source or sources (e.g. spelling standardization) –A text profile (): a detailed description of non-bibliographic aspects of a text –A revision history (): a record of changes to a file •Only is required to be TEI-compliant –The other three elements are optional •Tags can be nested, i.e. an element can appear inside another element The BNC header TEI guidelines •Markup languages adopted by the TEI –SGML (Standard Generalized Markup Language) –XML (eXtensible Markup Language) •Current version of TEI P5 guidelines (version 2.3.0, published in Jan 2013) •See the TEI official website for latest updates –http://www.tei-c.org/index.xml HTML, SGML, XML •HTML (Hypertext Markup Language) is based on SGML but with a predefined DTD (Document Type Definition) –HTML does not conform to all SGML rules (e.g. tags with no closing counterpart

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) •SGML: Standard Generalized Markup Language •XML is a simplified subset of SGML intended to make SGML easy enough for use on the Web –eliminating some of the more complex DTD constructs –introducing Unicode/multilingual support –(introducing data types and namespaces) XML Documents are trees • • • • • … • • • … • • Organizační diagram DOCUMENT PARAGRAPH PARAGRAPH PARAGRAPH SENTENCE SENTENCE WORD WORD Metadata in XML •What properties does a book have? –author, ISBN, publisher, number of pages, genre: fiction, etc • • • John Smith • CUP • Lost in translation • … • • •This contains “data” such as John Smith, CUP, Lost in Translation… –tags can have attributes (e.g. gender for author, type for book) • •It contains metadata (data about the data) in the form of tags • •Easy for a machine to know which pieces of information are about what Corpus Encoding Standard (CES) •Designed specifically for the encoding of language corpora –Document-wide mark-up •bibliographical description, encoding description, etc –Gross structural mark-up •volume, chapter, paragraph, footnotes, etc •specifying recommended character sets –Markup for sub-paragraph structures • sentence, quotations, words, MWUs, abbreviations, etc Corpus Encoding Standard •CES specifies a minimal encoding level that corpora must achieve to be considered as standardized in terms of descriptive representation as well as general architecture •3 levels of standardization designed to achieve the goal of universal document interchange –Metalanguage level regulates the form of the “syntactic” rules and the basic mechanisms of markup schemes (e.g. case sensitive, matching start/end tags) –Syntactic level specifies precise tag names and “syntactic” rules for using the tags –Semantic level ensures the same tag names are interpreted in the same way by the data sender and receiver e.g. vs. <h.title> Corpus Encoding Standard •Like the TEI scheme, CES not only applies to corpus markup, it also covers encoding conventions for the linguistic annotation of text and speech •Available in both SGML and XML –The expanded XML version is called XCES •See the CES official website for latest updates –http://www.cs.vassar.edu/CES/ Character encoding •Rarely an issue for English –ASCII (American Standard Code for Information Interchange) – “plain text” (ANSI: American National Standard Institute) –Special characters are exceptions, which are represented in SGML version of TEI and CES using entity references (included between ampersand and semi-colon) •£ = £ •é = é •The ISO-8859 family of 15 members –Complementary standardized character codes •Unicode (Unification Code) –Supported in XML –UTF-8 (8-bit Unicode transformation format) –UTF-16 (16-bit Unicode transformation format) •See Unicode official website for latest updates –http://unicode.org/ ASCII /aski/: American Standard Code for Information Interchange; ANSI: American National Standard Institute Character encoding •ASCII (ANSI), GB2312, Big5, UTF8, Unicode (UTF16) –For more details see http://ahds.ac.uk/creating/guides/linguistic-corpora/chapter4.htm •WordSmith 5 is based on Unicode (16-bit) –Unless your corpus is all ASCII characters, WST may NOT produce reliable results unless it is converted into Unicode –WST Utilities – Text Converter –MLCT or Textforever.exe for conversion •The combination of XML and Unicode is the current standards in corpus building (Xiao et al 2004) Text conversion Keep a safe copy of your text before you convert! http://download.pchome.net/utility/file/editor/detail-83578.html Data capture tools •Freeware tools that help you to download all pages at a selected website at one go –Grab-a-Site •http://download.cnet.com/Grab-a-Site/3000-2646_4-68934.html •HTTrack •http://www.httrack.com/ •Webgetter in WST 4.0 or 5.0 –WST menu – Utilities – WebGetter –Downloads all the pages containing the specified search word –But does not tidy up the data •Multilingual Corpus Toolkit (MLCT) –http://www.ling.lancs.ac.uk/corplang/cbls/zipfiles/MLCT.zip –Can download, tidy up and POS tag the selected webpage –Can markup textual organization automatically (<p>, <s>) WST WebGetter Using MLCT to capture web text http://www.zju.edu.cn/english/about/index.htm Using MLCT to capture web text Transcriber •A tool for assisting the manual annotation of speech signals –Segmenting long duration speech recordings –Transcribing audio recordings –Labelling speech turns, topic changes and acoustic conditions •Supporting multiple platforms –Windows XP/2k –Mac OS X –Linux •Downloading the programme, user manual, annotation guide –http://sourceforge.net/projects/trans/ Transcriber • Praat http://www.fon.hum.uva.nl/praat/download_win.html Well known and widely used (many online tutorials) Suitable for acoustic analysis of files that are shorter than 15 minutes Audacity Recording and editing sounds Can work with large files Digitalise your cassette tapes Download at http://audacity.sourceforge.net/ Voice walker: http://www.ruf.rice.edu/~reng/trans/voicewalker.html F4: http://www.audiotranskription.de/english/f4.htm