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Using Corpora for Language Research

Using Corpora for Language Research. COGS 523-Lecture 4 Using Corpora with Other Resources; Corpus Software. Related Readings. Readings: Buchholz and Green (2006); Miller and Fellbaum (2007); Sampson and McCarthy Ch 29. Extra – Information sheet for Resources

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Using Corpora for Language Research

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  1. Using Corpora for Language Research COGS 523-Lecture 4 Using Corpora with Other Resources; Corpus Software COGS 523 - Bilge Say

  2. Related Readings Readings: Buchholz and Green (2006); Miller and Fellbaum (2007); Sampson and McCarthy Ch 29. Extra – Information sheet for Resources Optional (can be used in software reviews!!) Garretson, G. (2008) Desiderata for Linguistics Software Design. International Journal of English Studies 8(1), 67-74. (The link is available on METU Online) COGS 523 - Bilge Say

  3. Lexical and Ontological Resources • Useful for Natural Language Processing, Pyscholinguistics, Corpus Annotation (eg automating semantic annotation) • A selected review is to follow, but there are others... COGS 523 - Bilge Say

  4. WordNet - Preliminaries • Lexeme vs Sense • Homonyms (Homophones or homographs): Words that have the same form with unrelated meanings • Polysemy: Multiple related meanings with a single lexeme (eg sperm bank) • Hard to distinguish between polysemy and homonymy sometimes. COGS 523 - Bilge Say

  5. WordNet - Preliminaries • Synonymy: Different lexemes, same (or nearly same) meanings • Hyponymy: A subclass of: poodle->dog; car -> vehicle (opp. direction hypernymy) • Mereonymy: A part of: leg -> table • Antonymy: Opposites COGS 523 - Bilge Say

  6. WordNet • A lexical database for English (and 30 other languages, see Balkanet and EuroWordnet projects); most extensive use: word sense disambiguation (Wordnet book available at the library) • Synsets: A set of synonyms • Each sense entry contains synsets, a dictionary style definition, some example uses (and a frequency number) • Four separate databases: nouns (hyponymy, meronymy), verbs (hyponymy,manner, causation, etc.), adjectives and adverbs • Synsets will be chained together with hyponynms and hypernyms – multiple chains possible COGS 523 - Bilge Say

  7. Bass -> musical instrument -> instrument -> device ....-> entity Bass -> singer, vocalist -> musician -> performer ....-> entity COGS 523 - Bilge Say

  8. Extensions • WordNetPlus: Dense Weighted X-database of automatically learned evocation (how much a certain concept brings to mind the second) ratings...First human-rated 120,000 pairs from 1000 synsets – most frequent concepts in BNC. • ImageNet: Enhancing WordNet with images and icons. COGS 523 - Bilge Say

  9. An example of Wordnet Query COGS 523 - Bilge Say

  10. Turkish WordNet project • http://www.hlst.sabanciuniv.edu/TL/ • Combined with phonetic rendering, morphological analysis, English equivalent etc. • http://www.ceid.upatras.gr/Balkanet/index.htm Part of Balkanet project for 6 Balkan languages • 12,000 synsets COGS 523 - Bilge Say

  11. An example of Turkish Wordnet Query COGS 523 - Bilge Say

  12. An Alternative to Turkish WordNet • 60000 hypernyms, 72 layers • Machine learning from TDK dictionary • Ongoing work, needs disambiguation • More coverage than Turkish WordNet • By Tunga Güngör and Onur Güngör, Boğaziçi Univ

  13. Ontologies - Cyc • A knowledge base of human commonsense and associated inference engine. • http://www.opencyc.org/ (Free version) http://research.cyc.com/ (Academic version) • Doug Lenat’s project – 1984+ • 300,000 concepts • Nearly 3,000,000 assertions (facts and rules), using 26,000+ relations, that interrelate, constrain, and, in effect, (partially) define the concepts. • Natural Language Query and Information Entry Tools COGS 523 - Bilge Say

  14. The graph representation of the Cyc Knowledge Base http://www.cyc.com/cyc/technology/whatiscyc_dir/whatdoescycknow COGS 523 - Bilge Say

  15. An example of a knowledge representation sample coded with CycL COGS 523 - Bilge Say

  16. ConceptNet • http://web.media.mit.edu/~hugo/conceptnet/ • Part of Open Mind Initiative • A huge wiki type of effort to create a commonsense knowledgebase represented as a semantic network • 1.6 million edges (assertions) connecting more than 300 000 nodes, where nodes are semi-structured English fragments. • interrelated by an ontology of twenty semantic relations such as EffectOf (causality), SubeventOf (event hierarchy), CapableOf (agent’s ability), PropertyOf, LocationOf, andMotivationOf (affect). COGS 523 - Bilge Say

  17. An excerpt from ConceptNet’s semantic network COGS 523 - Bilge Say

  18. from Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal COGS 523 - Bilge Say

  19. FrameNet • FrameNet is a lexicon-building project for English, based on frame semantics, carried out by International Computer Science Institute of University of Berkeley. • Frame: schematic representation of a situation type (eating, spying, removing, classifying, etc.) together with lists of the kinds of participants, props, and other conceptual roles that are seen as components of such situations. The semantic arguments of a predicating word correspond to what we call the frame elements(FE) of the frame associated with that word. COGS 523 - Bilge Say

  20. FrameNet • Uses BNC and ANC • Currently (version 1.3), there are more than 10,000 lexical units, more than 6,000 of which are fully annotated, in more than 800 hierarchically-related semantic frames, exemplified in more than 135,000 annotated sentences in the database. • WordNet – ConceptNet hybrid, with a grammar theory in the background (Fillmore’s Frame Semantics). COGS 523 - Bilge Say

  21. Interface of the Frame Grapher COGS 523 - Bilge Say

  22. Sample Output From Frame Grapher input: Crime_Scenario COGS 523 - Bilge Say

  23. Software for Working with Corpora “Corpus Linguistics in its current form cannot work without the help of the computer.” (Mason) • Acc. to Function: Corpus Building Software vs Corpus Query Software • Acc. to Design: Standard Software for Non-Technical Users vs Specialized Toolkits Providing Standard Functions vs Using Non-Corpus Specific Tools and Programming Languages (e.g. grep, egrep, perl, phyton, tcl/tk, java) COGS 523 - Bilge Say

  24. Corpus Software • Standard Software: MonoConcPro, WConcord, Wordsmith, IMS CQP (Corpus Query Processor, Qwick, Xaira, Gsearch • More General Purpose NLP Suites/Toolkits for Programmers: CUE (Corpus Universal Examiner), NLTK, GATE COGS 523 - Bilge Say

  25. Corpus Query/Analysis Software • Text Analysis Software -> Corpus Query Software -> Concordancers • Collocations in KWIC format (Keyword in Contex) • General Features • Search • Display, Save, Export • Statistics COGS 523 - Bilge Say

  26. Features • Search • Word, phrase, POS etc search • Regular expression search • Context-sensitive search • Header info search • Display, save, export • KWIC or sentence format • Sorting • Saving results or search patterns • Statistics • Frequency and various statistics • Plotting graphs COGS 523 - Bilge Say

  27. A Comparison Framework • Platform/Operating System • Price • Ease of Installation • User friendliness • Speed • Ease of setting up a corpus/texts • Query syntax • Query search power (collocational, discontinous constituents) • Statistical Analysis • Standard markup scheme handling • Whole text browsing • Character set handling • Output for presentation COGS 523 - Bilge Say

  28. Desiderata – some maxims • Do not build linguistic theory into the program any more than necessary • Do separate markup from annotation • Do not gloss over complexities in data – sensible defaults that can be overriden are fine • Allow users to supply their own analytical categories – e.g. Annotation of concordance lines • Make use of standards • Use Unicode COGS 523 - Bilge Say

  29. IMS Corpus Workbench (CWB) • http://www.ims.uni-stuttgart.de/projekte/CorpusWorkbench/ • IMS Corpus Query Processor (CQP): query system for CWB • Allowing use of multiple knowledge sources (corpora, machine readable dictionaries etc) • Allowing the use of stored information and calculating information on-line (from remote corpora) • Both for Human-Machine Use but not really for novice users... • Regular Expression based syntax. COGS 523 - Bilge Say

  30. From CWB web site Query language • unrestricted number of attributes per corpus position • regular expressions over attribute values of individual corpus positions (e.g. wild cards for word forms, part-of-speech values) • regular expressions over sequences of corpus positions • (partial) support of structural annotations (e.g. SGML) • incremental concordancing • application of a query to all items of a list • 'virtual attributes', i.e. runtime access to external applications (e.g. WordNet) • queries on parallel translated texts COGS 523 - Bilge Say

  31. From CWB web site Display of results • user-definable size of 'keyword in context' display • 'keyword in context' lines can be sorted in various ways • frequency counts, e.g. for word combinations • multilingual concordances from aligned corpora • html and latex output supported • query history COGS 523 - Bilge Say

  32. From CWB web site • registration of corpora • 'encoding' of corpora, i.e. indexing (and compression) (for text sources in one-word-per-line format, using ISO8859/Latin-1 8bit character sets, and maybe others) For example, the BNC corpus with part-of-speech and lemma annotation will need about 1 GB of disk space. • incremental addition of types of corpus annotations ('attributes'). E.g. add part-of-speech values to a corpus once you have access to a POS-tagger. COGS 523 - Bilge Say

  33. Regular Expressions • Equivalent to regular languages and finite automaton languages • Take empty language, languages with a single string, and apply concatenation, union or Kleene star operations on them. Everything you can generate in this way will be regular languages. (Partee et al., 1993) COGS 523 - Bilge Say

  34. Regular Expressions From CQP Tutorial... • Basic syntax of regular expressions • letters and digits are matched literally (including all non-ASCII characters) word word; C3PO C3PO; déjà déjà • . matches any single character (``matchall'') r.ng ring, rung, rang, rkng, r3ng, ... • character set: [...] matches any of the characters listed moderni[sz]e modernise, modernize[a-c5-9] a, b, c, 5, 6, 7, 8, 9[^aeiou] b, c, d, f, ..., 1, 2, 3, ..., ä, à, á, ... • repetition of the preceding element (character or group): ? (0 or 1), * (0 or more), + (1 or more), { } (exactly ), { , } ( ) colou?r color, colour; go{2,4}d good, goood, goood[A-Z][a-z]+ ``regular'' capitalised word such as British • grouping with parentheses: (...) (bla)+ bla, blabla, blablabla, ...(school)?bus(es)? bus, buses, schoolbus, schoolbuses • | separates alternatives (use parentheses to limit scope) mouse|mice mouse, mice; corp(us|ora) corpus, corpora COGS 523 - Bilge Say

  35. Regular Expressions Complex regular expressions can be used to model (regular) inflection: • ask(s|ed|ing)? ask, asks, asked, asking(equivalent to the less compact expression ask|asks|asked|asking) • sa(y(s|ing)?|id) say, says, saying, said • [a-z]+i[sz](e[sd]?|ing)  any form of a verb with -ise or -ize suffix COGS 523 - Bilge Say

  36. Some examples from CQP • the specified word is interpreted as a regular expression >"interest(s|(ed|ing)(ly)?)?"; • > [(lemma="under.+") & (pos="V.*")]; • a noun, followed by either is or was, followed by a verb ending in ed:[pos="N.*"] "is|was" [pos="V.*" & word=".*ed"]; • similar, but is or was followed by a past participle (which is described by a special POS tag):[pos="N.*"] "is|was" [pos="VBD"]; • catch or caught, followed by a determiner, any number of adjectives and a noun, or a noun, followed by was or were, followed by caught:"catch|caught" [pos="DT"] [pos="JJ"]* [pos="N.*"] | [pos="N.*"] "was|were" "caught"; • look or bring, followed by either up or down with at most 10 non-verbs in between:"look|bring" [pos != "VB.*"]{0,10} "up|down"; COGS 523 - Bilge Say

  37. Searching for more complex patterns • Gsearch Corpus Query System • http://www.hcrc.ed.ac.uk/gsearch/ • Facilitating the investigation of lexical and syntactic phenomena in unparsed but tagged corpora (can work with external taggers too) • Users specify their own context free grammar • Can take something like 167 minutes for a search on 100 million words BNC, • False positives should be manually eliminated • Visualization tools to display tree structures COGS 523 - Bilge Say

  38. Alternative: Using a class library • Mason, O. Programming for Corpus Linguistics: How to do text analysis with Java, Edinburgh University Press, 2000. • CUE (Corpus Universal Examiner): class library in Java that takes care of indexing, compressing large corpora, support for XML and Unicode • Qwick: a concordancing application that is developed using CUE COGS 523 - Bilge Say

  39. A Professional Alternative • http://athel.com/ • MonoConcPro ($95) • Features: Context Search, Regular Expression search, Part-of-Speech Tag Search, Collocations, and Corpus Comparison. • Not language specific • You can also buy a Chinese (and other languages) concordance T-shirt  COGS 523 - Bilge Say

  40. From an older version of MonoConc Pro COGS 523 - Bilge Say

  41. COGS 523 - Bilge Say

  42. Quality Control in Corpora • Format: Punctuation, delimiters, character encoding, • Presence and order of all fields, • Typos in labels and annotation. • Explicit Documentation • Format Checker – Structure Checker • Solution: Versioning and Patching mechanism in Treebanks and Corpora COGS 523 - Bilge Say

  43. Interrater agreements - reliability • Cochran’s Q test – binary values • Kappa – multivalued (Carletta, 1996) • Sensible chosen unit of agreement • Expert vs naive coders • K>0.8 good • Generalizability Theory (G-Theory) (Bayerl and Paul, 2007) – finer grained COGS 523 - Bilge Say

  44. Lecture 5 See articles on METU Turkish Corpus and Metu-Sabanci Treebank under Lecture Notes. COGS 523 - Bilge Say

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