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Corpus Linguistics Richard Xiao

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1 Corpus Linguistics Richard Xiao
Corpus annotation Corpus Linguistics Richard Xiao

2 Outline of the session Lecture Lab Rationale for corpus annotation
Leech’s maxims of corpus annotation Types of annotation Lab CLAWS POS tagger (online and Windows-based) Introducing Wmatrix ICTCLAS

3 Corpora and annotation
Unannotated corpus simple plain text or raw text the linguistic information is implicit e.g. no explicit representation of present as a noun Annotated corpus no longer just text real repository of linguistic information the relevant linguistic information is now explicit (e.g. present as a noun, adjective, or verb)

4 Corpus annotation What is annotation?
“The process of adding […] interpretive, linguistic information to an electronic corpus of spoken and/or written language data” (Leech 1997) Broadly, also refers to the results of the annotation process In a strict sense, different from corpus markup Markup provides objective, verifiable information e.g. author, paragraph boundary Annotation is concerned with interpretive linguistic information e.g. part-of-speech

5 Why annotate a corpus? It makes information retrieval and extraction easier, faster and enables human analysts to exploit and retrieve analyses of which they are not themselves capable Annotated corpora are reusable resources Annotated corpora are multifunctional - they can be annotated with a purpose and be reused with another Corpus annotation records a linguistic analysis explicitly 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

6 How are corpora annotated?
Automatic annotation Can be automated reliably for some types (POS, lemmatization) Can annotate large amount of data quickly at low cost Post-editing or human correction may be necessary to improve accuracy Computer-assisted annotation The semi-automatic annotation process (human-machine interface) may produce more reliable results than fully automated annotation, but it is also slower and more costly Manual annotation Occurs where no annotation tool is available or where the accuracy of available systems is not high enough to be useful Expensive and time-consuming, typically only feasible for small corpora

7 Leech’s 7 maxims of annotation
1. It should be possible to remove the annotation from an annotated corpus in order to revert to the raw corpus. 2. It should be possible to extract the annotations by themselves from the text. 3. The annotation scheme should be based on guidelines which are available to the end user. 4. It should be made clear how and by whom the annotation was carried out. The end user should be made aware that the corpus annotation is not error-free or infallible, but simply a potentially useful tool. 6. Annotation schemes should be based as far as possible on widely agreed and theory-neutral principles. 7. No annotation scheme has the a priori right to be considered as a standard. Standards emerge through practical consensus.

8 Types of corpus annotation
Phonological level Syllable boundaries (phonetic/phonemic annotation) Prosodic or suprasegmental features (prosodic annotation, e.g. pitch, loudness, intonation) Morphological level Prefixes, suffixes, stems (morphological annotation) Lexical level Tokenisation (essential for Chinese) Parts of speech (POS tagging) e.g. present: NN1, VVB, JJ Lemmas (lemmatization) stop, stopped, stops, stopping → stop Semantic fields (semantic annotation) cricket: sport, insect

9 Tokenisation The one-to-one correspondence between orthographic and morpho-syntactic word tokens can be considered as a default in English with three main exceptions Multiword units (e.g. so that and in spite of) Mergers (e.g. can’t and gonna) Variably spelt compounds (e.g. noticeboard, notice-board, notice board) CLAWS examples (“ditto tags”) so that: so_CS21 that_CS22 in spite of: in_II31 spite_II32 of_II33 can’t: ca_VM n’t_XX

10 Explosives found on Hampstead Heath.
BNC-style POS tagging <s> <w NN2>Explosives <w VVD>found <w PRP>on <w NP0>Hampstead <w NP0>Heath <PUN> </s> Explosives found on Hampstead Heath. new sentence plural noun past tense verb preposition proper noun proper noun punctuation

11 Example of semantic tagging
See for the tagset.

12 Types of corpus annotation
Syntactic level Parsing / treebanking / bracketing (S (NP Mary) (VP visited (NP a (ADJP very nice) boy))) Stanford Parser

13 Types of corpus annotation
Discourse level Anaphoric relations (coreference annotation) (6 the married couple 6) said that <REF=6 they were happy with <REF=6 their lot. Speech acts (pragmatic annotation) 3 layers of coding Segmentation (dividing dialogue in textual units, i.e. utterances) Functional annotation (dialogue act annotation) Utterance tags (applying utterance tags that characterize the role of the utterance as a dialogue act) Stylistic features such as speech and thought in presentation (stylistic annotation) The representation of people’s speech and thoughts, known as speech ad thought presentation (S&TP)

14 Types of corpus annotation
Other types Error tagging Applying to learner corpus data The CLEC error tagging scheme consists of 61 error types clustered in 11 categories Problems-specific annotation Not exhaustive – only the phenomenon directly relevant to a particular research question Developed for its relevance to the specific research question, but not for its broad coverage and consensus-based theory-neutrality E.g. Hunston (1993) studies how people talk about sameness and difference (“local grammar”)

15 Annotation styles Standalone style Embedded style - LOB style
<w id=“1”>He</w> <w id=“2”>was</w> <w id=“3”>going</w> <w id=“4”>to</w> <w id=“5”>die</w> <w id= “6”>.</w> </s> <word id=“1”>PPHS1</word> <word id=“2”>VBDZ</word> <word id=“3”>VVGK</word> <word id=“4”>TO</word> <word id=“5”>VVI</word> <word id=“6”>.</word> Embedded style - LOB style going_VVGK TEI entity references going&VVGK; WSJ style going/VVGK SGML <w POS=VVGK>going</w> BNC style (simplified SGML) <w VVGK>going XML <w POS=“VVGK”>going</w>

16 Introducing CLAWS CLAWS: some basic facts
The Constituent Likelihood Automatic Word-tagging System Best known POS tagger for general English Has been used to tag a number of large corpora, including the 100M word BNC Has consistently achieved 96-97% accuracy Free online tagging service allow academic users to tag 100,000 word at a time (from an academic website)

17 CLAWS tagsets C7 taget C5 tagset
A detailed tagset of 146 tags C5 tagset Less refined, 61 tags (BNC tagset) The mapping between C7 and C5 is a many-to-one conversion, and is available in a tab-delimited text file C8 tagset is an extension of C7 tagset that makes further distinctions in the determiner and pronoun categories as well as for auxiliary verbs

18 Free CLAWS trial service

19 CLAWS output formats Vertical output format
Horizontal output format (Use copy & paste and save as a plain text file) Pseudo-XML output format

20 Windows-based CLAWS D:\ZJU CL\tools\Jclaws\lib\run_jclaws.bat (or antclawsgui) …tagging text in a file

21 Wmatrix An online corpus analysis and comparison system
A web interface that allows you to access to the CLAWS part-of-speech tagger and the USAS semantic tagger CLAWS USAS: UCREL Semantic Analysis System Including standard corpus research tools Frequency, KWIC concordance, wordlist, keyword list, word cluster/n-gram), collocation Built-in statistics model log likelihood for corpus comparison Integrating POS tagging and semantic field annotation into a single profiling tool Introduction to Wmatrix

22 Your Wmarix account You will need a username and password to use Wmatrix Write down your username and password Tag and download your text as soon as possible if you wish to use Wmatrix to tag your data (POS / semantic) on your project …and now login with your account

23 Click here to run “tag wizard”
Click here to see your work area (for data you have already processed) Click here to find out more about the UCREL Semantic Annotation System

24 Amongst other things, the link explains the categorisation scheme utilised …
McArthur (1981) Longman Lexicon of Contemporary English Hierarchy of 21 major discourse fields (or domains), which expand into 232 semantic field tags (see the web link) semantic field (or domain) = “A named area of meaning in which lexemes interrelate and define each other in specific ways” (Crystal 1995: 157) Note --- the USAS scheme is derived from McArthur (1981)

25 The USAS system Designed to undertake the automatic semantic analysis of present-day English texts (spoken and written) Involving two stages (i) POS tagging by CLAWS A POS tag is assigned to every lexical item or multi-word expression (MWE), using probabilistic Markov models of likely part-of-speech sequences (accuracy of 97%+) (ii) Output fed into SEMTAG for semantic annotation Semantic tags are assigned automatically on the basis of pattern matching between the target text and two computer dictionaries developed for use with the program (accuracy of 92%+) Present applications: market research, content analysis, information extraction, assistance for translation, linguistic analysis, etc. MWE = phrasal verbs, compound nouns, multi-word proper nouns, pure idioms.

26 Let’s do some tagging Once you have logged in:
From the Wmatrix home page, click on Tag wizard This will bring up the following page …

27 Let’s do some tagging Tag the following two texts:
Tips: It’s a good practice to create one folder for each file Conservative MP Michael Howard’s farewell speech to his party (2005) D:\ZJU CL\texts\Howard_speech.txt New Labour MP Tony Blair’s farewell speech to his party (2006) D:\ZJU CL\texts\texts\Blair_speech.txt

28 A quick “how to”! Enter new work area name (Blair / Howard)
Click the browse button to select the right file Click the “upload now” button … A new screen will provide you with an update report … e.g. part-of-speech tagging semantic tagging frequency lists

29 You will then be taken to your work area [My folders]

30 What you’ll see in the Simple “VIEW of folder”
Click on Frequency to see the most frequent words You can also do concordance searches of words/phrases

31 Advanced View of Howard Folder
Click on Frequency to see the most frequent words (as before) --- and investigate key parts of speech (POS) and key concepts / domains How might we discover the most ‘frequent’ POS? Jot them down --- and the most ‘frequent’ semantic fields? Make a note of them We can also see all of the keywords using this VIEW

32 Frequency of words in Howard and Blair (using advanced view)
Make a note of the similarities and differences …

33 Download the tagged text
Remember to change filename and file type

34 Tagging Chinese text ICTCLAS – Institute of Computing Technology, Chinese Lexical Analysis System Best Chinese tagger Fast and reliable (98.45%) Online demonstration Free download of shareware version

35 Online demo

36 Standalone ICTCLAS D:\ZJU CL\tools\ICTCLAS\ICTCLAS_Win.exe
Tagset -

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