Using corpora for language research
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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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Using corpora for language research

Using Corpora for Language Research

COGS 523-Lecture 4

Using Corpora with Other Resources;

Corpus Software

COGS 523 - Bilge Say


Related readings

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)

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Lexical and ontological resources

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...

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Wordnet preliminaries

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.

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Wordnet preliminaries1

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

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Wordnet

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

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Using corpora for language research

Bass -> musical instrument -> instrument -> device ....-> entity

Bass -> singer, vocalist -> musician -> performer ....-> entity

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Extensions

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.

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Using corpora for language research

An example of Wordnet Query

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Turkish wordnet project

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

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Using corpora for language research

An example of Turkish Wordnet Query

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An alternative to turkish wordnet

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


Ontologies cyc

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

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Using corpora for language research

The graph representation of the Cyc Knowledge Base

http://www.cyc.com/cyc/technology/whatiscyc_dir/whatdoescycknow

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Using corpora for language research

An example of a knowledge representation sample

coded with CycL

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Conceptnet

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).

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Using corpora for language research

An excerpt from ConceptNet’s semantic network

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Using corpora for language research

from Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal

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Framenet

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.

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Framenet1

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).

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Using corpora for language research

Interface of the Frame Grapher

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Using corpora for language research

Sample Output From Frame Grapher

input: Crime_Scenario

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Software for working with corpora

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)

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Corpus software

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

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Corpus query analysis software

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

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Features

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

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A comparison framework

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

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Desiderata some maxims

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

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Ims corpus workbench cwb

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.

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From cwb web site

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

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From cwb web site1

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

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From cwb web site2

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.

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Regular expressions

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)

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Regular expressions1

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

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Regular expressions2

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

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Some examples from cqp

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";

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Searching for more complex patterns

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

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Alternative using a class library

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

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A professional alternative

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 

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Using corpora for language research

From an older version of MonoConc Pro

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Using corpora for language research

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Quality control in corpora

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

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Interrater agreements reliability

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

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Lecture 5

Lecture 5

See articles on METU Turkish Corpus and Metu-Sabanci Treebank under Lecture Notes.

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