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Linguistic ResearchAnd

The CLARIN Infrastructure

Jan Odijk

Digital Humanities Lecture, Utrecht 23 Oct 2012

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Overview

• Introduction• Basic Facts & Research Questions• Do the Research

– Consult Grammars– Select from relevant data from multiple sources– Apply tools to enrich data– Analyze the data

• Conclusions

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Introduction

• Suppose you’re a linguistic researcher in 1980 (no internet, no computers,…)– And libraries would not exist….

• I am a linguistic researcher in 2012– But no infrastructure for data and tools exists!– though there are many data and tools

• CLARIN has as its main goal to remedy this

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Basic Facts

• Heel, erg, and zeer are synonyms (‘very’)

• Zeer, erg can modify verbs, adjectival predicates and prepositional predicates

• Heel can only modify adjectival predicates– A: Hij is daar zeer/erg/heel blij mee – P: Hij is daar zeer/erg/*heel mee in zijn nopjes– V: Dat verbaast ons zeer/erg/*heel.

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Basic Facts

• English very is like heel in these respects;– P: *He is very in love– A: He is very amorous – V: It surprised us very *(much))

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Basic Facts

• Difference: – not due to semantics– Purely syntactic– As far as we know: does not follow from a general

rule– So it must be ‘learned’ by a child acquiring Dutch

as first language

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Research Question (1)

• How does a child acquiring Dutch as a first language get to ‘know’ that zeer and erg can modify verbs, prepositional and adjectival predicates?

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Hypotheses (1)

• Hypothesis 1a– Once a word is encountered for the first time, a

critical phase (‘training phase’) starts in which the word properties will be determined based on input; after this phase the word properties are fixed.

– A sufficient number of actual examples occurring in this period sets the word properties (positive evidence)

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Hypotheses (1)

• Hypothesis 2a– Once a word is encountered for the first time, its

grammatical properties are initially set by Semantic Bootstrapping: D (semcat) -> syncat

– A sufficient number of actual examples occurring in this period will add to the word properties (positive evidence)

– Sufficient amount of input that is contradictory to the semantically bootstrapped properties overrules them

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Research Question (2)

• How can a child acquiring Dutch as first language get to ‘know’ that heel cannot modify prepositional predicates and verbs?– Children are never taught that it is not possible;– They are also never or seldom corrected for

language errors, and if they are, they seem to ignore it (Negative evidence plays no role)

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Hypotheses (2)

• Hypothesis 1b– Absence of relevant constructions in the training

phase of a word leads to absence of the property (indirect negative evidence)

• Hypothesis 2b– Absence of relevant constructions in the training

phase of a word does not lead to absence of the property for semantically bootstrapped properties

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Related Questions

• Do children ever make errors against this?• Is a ‘training phase’ for word properties real?• How ‘long’ is this training phase? • What is a ‘sufficiently large’ number of actual

examples• Does semantic bootstrapping play a role, and if so

which one• Are these words acquired in different language

acquisition stages?

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Related Questions

• Can this be related to the different modification potential?

• Is there a relation with the fact that zeer appears to be rather formal, while heel and erg are not?

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Related Questions

• adverb-adjective agreement (substandard): – heel/hele dikke boeken ‘very thick books’– erg/erge dikke boeken– Zeer/*zere dikke boeken– Is this somehow related?

• What about other, closely related, words?

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Consult Grammars

• Currently– Consult paper and electronic grammars

• ANS and e-ANS e.g. section 15-3-1-1

• In the near Future– Consult Taalportaal with (I hope/expect)

• All examples formally marked as such • All examples parsed/tagged, using ISOCAT DCs and

searchable• Links to (possibly complex queries) to illustrate with real

data from treebanks and other annotated data

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Find Data

• Which data and tools (LRs) exist that might contribute to answering these questions?

• Currently:– you have to search for them in multiple places– Many relevant data are not publicly visible (you

will encounter them by personal contacts only)– Or you have to create them yourself

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Find Data

• There is no place/site where you query:– Give me a list of all LRs for the Dutch language– What is the size of all Dutch text corpora (in #tokens)– Give me a list of all Dutch data that contain children

2-7 years old as speaker– Give me a list of all Dutch data containing any of the

words heel, zeer, erg

• Not even in most individual data centres (TST-Centrale, ELRA, LDC, ..)

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Find Data

• CLARIN– Provides a flexible framework incl. tools for

making descriptions of LRs (‘metadata’)• CMDI

– Supports (assistance, execution, funding) the creation of metadata for LRs

– Supports making these metadata (and the actual data) visible and accessible via CLARIN portals

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Find Data

• CLARIN– Provides facilities for semantic interoperability

• ISOCAT, Relation Registry (coming soon)

– browsing, searching and querying facilities for the metadata

• Initial prototype: Virtual Language Observatory

– Will enable you to collect the data that are relevant to you in a virtual collection

– This will save the researcher a lot of time– It will enlarge the empirical basis for the research

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Closely Related Words

• Find words that are closely related– Adverbs that function as an intensifier (‘booster’)– Are (near-)synonymous, hyponyms, or co-

hyponyms– Also (near-)antonyms are relevant

• In order to determine their properties and potential further generalizations

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Closely Related Words

• Using e.g. – Synonym information in traditional dictionaries– Dutch EuroWordnet (currently via ELRA M0016)– Or Cornetto (via the Dutch HLT-Agency)

• Currently searchable only via – a plug-in in an old version (3.5) of Firefox. or – In programs via a python module

• A CLARIN-NL project to improve this

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Closely Related Words

Found via synonym dictionaries:• abnormaal afschuwelijk akelig bijster bijzonder

bovenmatig buitengemeen buitensporig danig donders eminent enorm exceptioneel extra extraordinair extreem fabelachtig fenomenaal geweldig gigantisch intens kolossaal merkwaardig mirakels onbeschrijfelijk ongelofelijk ongehoord ongekend ongemeen onmenselijk onmetelijk ontzettend onwijs speciaal uitermate uiterst uitzonderlijk verdraaid verduiveld verrekte verschrikkelijk vet zeldzaam …..

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Closely Related Words

• zeer:adverb:3 / heel:adverb:5 (from Cornetto)• zeer:3/d_r-343077, allemachtig:2/d_r-9922, beestachtig:2/d_r-23835, bijzonder:4/c_546765,

bliksems:2/d_r-32612, bloedig:2/d_r-32881, bovenmate:1/d_r-36728, buitengewoon:2/d_r-39235, buitenmate:1/d_r-39294, buitensporig:2/d_r-401837, crimineel:4/d_a-53026, deerlijk:2/d_r-57321, deksels:2/d_r-57728, donders:2/d_r-62605, drommels:2/d_r-65820, eindeloos:3/c_546740, enorm:2/d_r-74285, erbarmelijk:2/d_r-74877, fantastisch:6/d_r-79264, formidabel:2/d_r-82704, geweldig:4/d_r-92392, goddeloos:2/d_r-94633, godsjammerlijk:2/d_r-94798, grenzeloos:2/d_r-96846, grotelijks:1/d_r-98244, heel:5/d_r-106880, ijselijk:2/d_r-118854, ijzig:4/c_546756, intens:2/d_r-123517, krankzinnig:3/d_r-142403, machtig:4/d_r-165866, mirakels:1/d_r-173095, onsterachtig:2/d_r-175264, moorddadig:4/d_r-175475, oneindig:2/d_r-193740, onnoemelijk:2/d_r-194761, ontiegelijk:2/d_r-415154, ontstellend:2/d_r-415165, ontzaglijk:2/d_r-415176, ontzettend:3/d_r-196906, onuitsprekelijk:2/d_r-415180, onvoorstelbaar:2/d_r-415191, onwezenlijk:2/d_r-197464, onwijs:4/d_r-197468, overweldigend:2/d_r-205004, peilloos:2/d_r-213144, reusachtig:3/d_r-239357, reuze:2/d_r-239379, schrikkelijk:2/d_r-256144, sterk:7/d_r-272639, uiterst:4/d_r-300933, verdomd:2/d_r-308293, verdraaid:4/c_546761, verduiveld:2/d_r-308522, verduveld:2/d_r-308569, verrekt:3/d_r-418644, verrot:3/d_r-418648, verschrikkelijk:3/d_r-312634, vervloekt:2/d_r-314372, vreselijk:5/d_r-323099, waanzinnig:2/d_r-329061, zeldzaam:2/d_r-419882, zwaar:10/d_r-347153

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Basic Facts: Correct?

• Check the basic facts• Check against occurrences in corpora

– Problem: each of the 3 words is ambiguous!• Erg (4x)= noun(de) ‘erg’; noun(het)’evil’, adj+adv

‘unpleasant’, adv ’very’• Zeer (3x)= noun ‘pain’; adj ‘painful’; adv ‘very’• Heel (3x) = adj ‘whole’; verbform ‘heal’; adv ‘very’

– PoS-tagged corpus will help somewhat• But most corpora do not distinguish adj from adv by

category! (searching for PoS bigrams will help slightly)

– A fully-parsed corpus would be ideal

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Basic Facts: Correct?

– LASSY Small: 1M manually verified parsed corpus– Interface to LASSY Small

• Requires knowledge of XPATH/XQUERY

– Very Simple Interface to LASSY Small• limited options but simple commands

– Example-based interface GrETEL (CLARIN- Flanders)

• Greedy Extraction of Trees for Empirical Linguistics• Generates XPATH/XQUERY expression on the basis of

an example sentence plus markings of what is relevant in it

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Basic Facts: Correct?

– Queries: erg::mod:; zeer::mod: ; heel::mod:– Extract from Statistics:

– Query: heel::mod:ww

erg zeer heel

ADJ 143 268 263

WW 35 49 9

BW 1 1 7

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Basic Facts: Correct?

• Analysis– 8 examples are forms that are ambiguous between adjectival and verbal participle,

• All are examples of adjectival participles but LASSY represents all participles as verbal

– In 1 example heel modifies the adj open from the expression open staan voor, but wrongly analyzed as modifying the verb staan

• CLARIN will offer facilities to make annotations to such corpora• Same queries could be done

– for the other related words– on LASSY Large Corpus (2.4 billion words, automatically parsed)– In the CGN corpus (but it uses a different interface)

• But this will require facilities for ‘batch jobs’ or more complicated queries (maybe via web services)

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Acquisition Corpora: Search

• E.g. data in the CHILDES system (part of TalkBank– 7 corpora for Dutch– But with their own data formats (CHAT) and tools (

CLAN)

• However, also mirrored at MPI and accessible via (ANNEX/)TROVA (again another interface)

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Acquisition Corpora: Search

• Give records for utterances containing erg with– Corpus (e.g. Van Kampen Corpus)– File: (e.g. laura74.cha)– Line: (e.g. 139)– Part Role: (e.g. Child)– Child Gdr: (e.g. female)– Age: (e.g. 5;6.12)– UTT (e.g. “ja , die s erg moeilijk .”)

• Maybe also some preceding/following context• Map attribute names and values to ISOCAT

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Acquisition Corpora: Search

• Corpus: Van Kampen• File: sarah21.cha• Line: 630• Speaker: Child• Child Gender: Female• Age: 2;7.16• UTT: “prinses e(r)g groot !”

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Acquisition Corpora: Search

• For each child, give list of pairs session + age of the child

• For child and each session, give #occurrences of zeer, heel, erg

• etc, etc.• Such queries (Some example attempts )

– Mixed metadata/content search– Over multiple resources– Specific output formats

• are not so easy with the current interfaces!!

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Acquisition corpora: Search

• Heel is found 153 times in Van Kampen corpus• Erg is found 77 times in Van Kampen corpus

– But many are an irrelevant use of erg

• PoS-tagging the corpus might be useful– Search for POS-bigrams (e.g. erg/adj */adj)– Add lemma’s

• Or even full parsing, at least of the adult speech

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Acquisition corpora: Parse

• CLARIN-NL – Web services are being developed

• For PoS-tagging text• For full parsing of text• (and many more)

– To be usable by humanities researchers – in a user-friendly way in work flow systems

• Usefulness depends on– Size of the data (effort to select manually)– Quality of the web services

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Store the found data

• The found and newly created data – should be stored in a supported format– With automatically generated metadata– With automatically generated provenance data– Using data categories mapped to or from ISOCAT– For which PIDs are provided– Stored on a server of a CLARIN-centre– So that they

• can become proper resources on their own• Are visible, accessible and interpretable as part of enriched

publications

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Search in CGN / SONAR

• To assess level of formality– Give absolute and relative frequencies of

heel/hele/erg/erge/zeer as adj by text genre, and speaker/participants education level

– In CGN (spoken corpus)– In SONAR (written corpus)– Idem but for the word + the following Pos-tag– Idem but in the fully parsed part of CGN and in

LASSY + the PoS tag of the modifiee head

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Interpret the data

• Interpret the data in function of the hypotheses being investigated

• Apply analytical / statistical tools to the data– CLARIN should support formats of frequently used

statistical packages such as SPSS, R, etc.

• The research will surely lead to new questions, so to new queries

• Reach conclusions and publish an open access enriched publication

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Broaden the scope

• Do the same for worden/raken (‘become’/ ‘get’)

• NP, PP and AP can be predicate complements

• Worden and raken take predicate complements

• They are (almost) synonymous• worden: takes only NP or AP• raken: takes only AP or PP

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Broaden the scope

• AP: Zij werd / raakte zwanger• PP: Zij *werd / raakte in verwachting• NP: Zij werd / *raakte burgemeester

Andrepeat the process

Exercise

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Conclusions

• There is no adequate infrastructure for linguistic research

• There are bits and pieces, but– Finding LRs is not easy– LRs have their own formats, data categories, user /

search interfaces– Limited formal and no semantic interoperability– Search in combined LRs very difficult if not impossible

full research potential is not exploited• CLARIN(-NL) attempts to remedy this

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CLARIN-NL

Thanks for your attention!

http://www.clarin.nl/

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No Entry!

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Basic Facts: Correct?

• De omgang met de buren gebeurt op een heel ontspannen manier en de vrouw van de dominee heeft zelfs al Wolderse vlaai leren bakken . (parse) – heel:ADJ:mod:WW:ontspannen

• De verschijnselen zijn heel verschillend . (parse) – heel:ADJ:mod:WW:verschillend

• ,, Op het voorterrein ging het nog heel overtuigend . (parse) – heel:ADJ:mod:WW:overtuigend

• Ze hebben heel gericht en planmatig volkscafés bezocht om daar hun gif te spuien . (parse) – heel:ADJ:mod:WW:gericht

• Ze is zelfs met een ' meester ' getrouwd : Marc Dassesse _ mevrouw Spiritus-Dassesse zet heel geëmanicipeerd haar meisjesnaam voorop _ is nu een gerenomeerd fiscaal adviseur en hoogleraar aan de ULB . (parse)

– heel:ADJ:mod:WW:geëmanicipeerd • Gelukkig krijg ik nog heel geregeld te horen : ' Gerard jongen , dat doe je gewoon foùt ' . (parse)

– heel:ADJ:mod:WW:geregeld • Dat is een heel verrassend resultaat en het stemt tot optimisme . (parse)

– heel:ADJ:mod:WW:verrassend • De biermarkt is heel versnipperd en wordt overspoeld door nieuwe productlanceringen . (parse)

– heel:ADJ:mod:WW:versnipperd • Toch staan we hier heel open voor voorstellen . (parse)

– heel:ADJ:mod:WW:staan

Metadata search CGN+CHILDESDutch && 2<age<7

Regexp content searchheel|zeer|erg|erge|hele

Resultset export to file

CGNregexp ^heel$|^erg$|…

CGNregexp op WORDS tier + POS

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Exercise

• ‘Worden takes APs not PPs as predc’ • Use the LASSY-Small Very Simple Interface• Give me all sentences in which the word

“worden” takes a predicative (predc) PP complement:– rel='predc' and hlemma='worden‘ and postag='vz'

• Do you find examples with this query?• How do you interpret this?