Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

350 Pages·2016·11.919 MB·English

by  Alexander Mehler, Andy Lücking, Sven Banisch, Philippe Blanchard, Barbara Job (eds.)

Understanding Complex Systems
Alexander Mehler
Andy Lücking
Sven Banisch
Philippe Blanchard
Barbara Frank-Job Editors
Towards a Theoretical
Framework for
Analyzing Complex
Linguistic Networks
Springer Complexity
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EditorialandProgrammeAdvisoryBoard
HenryAbarbanel,InstituteforNonlinearScience,UniversityofCalifornia,SanDiego,USA
DanBraha,NewEnglandComplexSystems,InstituteandUniversityofMassachusetts,Dartmouth,USA
PéterÉrdi,CenterforComplexSystemsStudies,KalamazooCollege,USAandHungarianAcademyof
Sciences,Budapest,Hungary
KarlFriston,InstituteofCognitiveNeuroscience,UniversityCollegeLondon,London,UK
HermannHaken,CenterofSynergetics,UniversityofStuttgart,Stuttgart,Germany
Viktor Jirsa, Centre National de la Recherche Scientifique (CNRS), Université de la Méditerranée,
Marseille,France
JanuszKacprzyk,SystemResearch,PolishAcademyofSciences,Warsaw,Poland
KunihikoKaneko,ResearchCenterforComplexSystemsBiology,TheUniversityofTokyo,Tokyo,Japan
ScottKelso,CenterforComplexSystemsandBrainSciences,FloridaAtlanticUniversity,BocaRaton,
USA
Markus Kirkilionis, Mathematics Institute and Centre for Complex Systems, University ofWarwick,
Coventry,UK
JürgenKurths,NonlinearDynamicsGroup,UniversityofPotsdam,Potsdam,Germany
AndrzejNowak,DepartmentofPsychology,WarsawUniversity,Poland
HassanQudrat-Ullah,SchoolofAdministrativeStudies,YorkUniversity,Canada
LindaReichl,CenterforComplexQuantumSystems,UniversityofTexas,Austin,USA
PeterSchuster,TheoreticalChemistryandStructuralBiology,UniversityofVienna,Vienna,Austria
FrankSchweitzer,SystemDesign,ETHZürich,Zürich,Switzerland
DidierSornette,EntrepreneurialRisk,ETHZürich,Zürich,Switzerland
StefanThurner,SectionforScienceofComplexSystems,MedicalUniversityofVienna,Vienna,Austria
Understanding Complex Systems
Founding Editor: Scott Kelso
Futurescientificandtechnological developments inmanyfieldswillnecessarily dependuponcoming
togripswithcomplexsystems.Suchsystemsarecomplexinboththeircomposition—typically many
differentkindsofcomponentsinteractingsimultaneouslyandnonlinearlywitheachotherandtheirenvi-
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·
Alexander Mehler Andy Lücking
·
Sven Banisch Philippe Blanchard
Barbara Frank-Job
Editors
Towards a Theoretical
Framework for Analyzing
Complex Linguistic Networks
ABC
Editors
AlexanderMehler PhilippeBlanchard
Goethe-UniversityFrankfurtamMain DepartmentofPhysics
DepartmentofComputerScience UniversityofBielefeld
andMathematics Bielefeld
FrankfurtamMain Germany
Germany
BarbaraFrank-Job
AndyLücking FacultyofLinguistics&LiteraryStudies
Goethe-UniversityFrankfurtamMain UniversityofBielefeld
DepartmentofComputerScience Bielefeld
andMathematics Germany
FrankfurtamMain
Germany
SvenBanisch
MaxPlanckInstituteforMathematics
intheSciences
Inselstrasse22
D-04103Leipzig
Germany
ISSN1860-0832 ISSN1860-0840 (electronic)
UnderstandingComplexSystems
ISBN978-3-662-47237-8 ISBN978-3-662-47238-5 (eBook)
DOI10.1007/978-3-662-47238-5
LibraryofCongressControlNumber:2015940024
SpringerHeidelbergNewYorkDordrechtLondon
(cid:2)c Springer-VerlagBerlinHeidelberg2016
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Introduction
AlexanderMehler,AndyLu¨cking,SvenBanisch,
PhilippeBlanchard,andBarbaraFrank-Job
1 Onthe Content ofThis Book
Currently,weobserveanadventofapproachestoanalyzinglinguisticnetworkswith
themethodsofstochasticphysicsandgraphtheory.Generallyspeaking,alinguis-
tic network is represented by a graph whose vertices denote linguistic units (e.g.,
words, sentences, or textual units) and whose edges modellinguistic (e.g. syntac-
tic,semanticorpragmatic)relationsoftheseunits.Theaimofmodelsoperatingon
such networks is to capture the synchronic, topologicalor evolutionary dynamics
oflinguisticsystems, say,onthephonological,morphological,syntactic,semantic
orpragmaticlevel.Whatthese approacheshaveincommonisthattheymodelthe
structuralor temporaldynamicsof linguistic systems in orderto test information-
theoreticalorlinguistichypothesesonthegroundsofcomplexnetworktheory.This
ispartlydoneintermsofastrongnetworkperspectiveaccordingtowhichthenet-
workapproachisseentobeindispensabletotestthefocalhypotheses.Apparently,
the area of language evolution provides a good test case for such an approach.
AlexanderMehler·AndyLu¨cking
Goethe-UniversityFrankfurtamMain,DepartmentofComputerScienceandMathematics,
FrankfurtamMain,Germany
e-mail:{Mehler,Luecking}@em.uni-frankfurt.de
SvenBanisch
MaxPlanckInstituteforMathematicsintheSciences,Inselstrasse22,
D-04103Leipzig,Germany
e-mail:[email protected]
PhilippeBlanchard
FacultyofPhysics,BielefeldUniversity,Germany
e-mail:[email protected]
BarbaraFrank-Job
FacultyofLinguistics&LiteraryStudies,UniversityofBielefeld,Bielefeld,Germany
e-mail:[email protected]
VI A.Mehleretal.
Language evolution can be seen as a meso system that connects language as a
macro system with the micro system of cognitive processes of language process-
ing.Startingfromsuch a unifiedapproachto languagestructure,languagechange
andprocessing,networkapproachestry to gaininsightsinto the lawsof linguistic
informationprocessingincommunitiesofsocialagents.
Inspiteoftheremarkablesuccessregardingthedevelopmentofexpressivegraph
modelsoflinguisticsystems,theseapproachesarestillinneedofaunifyingframe-
work.Todate,themodelsareconnectedbyacommonmethodicalstancebasedon
complexnetworktheoryinadditiontoquantitativelinguistics.Thus,wefacearange
of diverse network models that focus on laws of information processing without
clarifyingtheirsynergeticinterdependencies.Thisispartlyduetothelackofshared
standardsofdatamodeling,oftheinteroperabilityofalgorithmicgraphmodelsand
ofthesustainabilityoftheunderlyinglinguisticresourcesandcorpora.Obviously,
interdisciplinary research across the boarder of computer science, linguistics and
stochasticphysicsmayprofitfromtheavailabilityofsuchstandards.
This book aims at making first steps into the direction of filling this gap. It
presentstheoreticaland empiricalresultsin supportofa unifyingapproachto lin-
guistic networks that may help to overcome bottleneck problems of this field of
research.Tothisend,thebookcomprisesrecentresearcheffortsintheareaoflin-
guisticnetworks.Itbringstogetherscientistswithdiversebackgroundsrangingfrom
linguisticstotext-technology,fromcomputationalhumanitiestostatisticalnetwork
theory.The bookis organized,roughly,into six partsincludingsemanticand syn-
tactic networks, the interplay of language and cognition, the simulation of socio-
linguisticdynamicsandtext-technologicalresourcesofnetworkmodeling.Special
emphasisis putoncritical articlesand articlesthat reviewrecentdevelopmentsin
thefield.Thisincludesthefollowingfieldsofresearch:
(cid:129) Resourcesoflinguisticnetworkanalysis.
(cid:129) Principlesoflinguisticnetworkinduction.
(cid:129) Topologicalmodelsoflanguagestructure.
(cid:129) Modelsoflanguagedynamics:evolution,diachrony,change.
(cid:129) Unifiedmodelsfromstochasticphysics.
(cid:129) Networkmodelsfromcognitivelinguistics.
(cid:129) Networkmodelsofphonological,lexical,syntactic,semanticorpragmaticsys-
tems.
(cid:129) Networkmodelsoftextsystemsincontrasttolanguagesystems.
Dealingwiththeseandrelatedtopics,theaimofthebookistoadvocateandpromote
networkmodelsoflinguisticsystemsthatarebothbasedonthoroughmathematical
modelsandsubstantiatedintermsoflinguisticinterpretations.Inthisway,thebook
contributesfirststepstowardsestablishingastatisticalnetworktheoryasatheoret-
ical basis of linguistic network analysis across the boarderof the naturalsciences
andthehumanities.
Introduction VII
2 Overview oftheBook
2.1 PartI:Cognition
Successfulapplicationsofnetworkanalysiswithaparticularfocusontheinterplay
of language and cognition are reviewed in the chapter of Beckage and Colunga.
Concentratingonsemanticandphonologicalnetworks,itexploresnetworkfeatures
andtheirrelationtohumanlanguageperformanceincludingtheapplicationtocog-
nitiveimpairmentandatypicalbehavior.
The chapter by Vitevitch, Goldstein and Johnson combines network tools and
datafromapsycholinguisticexperimenttoexplorespeechperceptionerrorswiththe
aimtounderstandbetterwhatisperceivedwhenaspokenwordismisperceived.The
experimentalresultsoftheirphonologicalassociationtaskareevaluatedintermsof
path’onanetworkofphonologicalsimilarity.
The chapter by De Deyne, Verheyen and Storms compares semantic networks
derivedfromtextcorporawithnetworksobtainedthroughwordassociationexper-
iments by looking at macro- and mesoscopic properties of both types of graphs.
Whiletheanalysisrevealsstructuralsimilaritiesatthegloballevel,significantdif-
ferencesbetweentextandwordassociationgraphsemergeatalowerlevelofcom-
munitystructureorcentrality.Thechapteralsopresentsacomparisonwithhuman
relatednessjudgments.
2.2 PartII:Topology
The chapter by Biemann, Krumov, Roos and Weihe presents a statistical analysis
ofthemotifsignaturesofco-occurrencegraphsincludingco-authorshipnetworks,
communication networks and linguistic co-occurrence graphs of natural and arti-
ficial languages. Based on the hypothesis that different word classes serve differ-
entfunctionsinalanguageananalysisofco-occurrencegraphsfordifferentword
classes(verbsvs.nounsvs.adjectivesetc.)isperformedwhichshowsthatespecially
verbsaredistinguishablefromotherwordclassesbytheirmotifsignature–across
differentlanguages.
ThechapterbyArau´joandBanischhighlightstheneedtoconsiderdifferentways
ofnetworkinductioninnetwork-basedanalysisoflanguageandreasonsthatinduc-
tionandanalysisarestronglyinterdependenttasks.Basedonaframeworkcompris-
ing differentabstractionlevelsalongwith levelsof statistical analysis,the authors
arguethatthefieldoflinguisticnetworksischallengedbythefactthataninterpre-
tation of topological indicators used in network analysis becomes the harder, the
highertheabstractionlevelofthenetwork.
The chapter by Masucci, Kalampokis, Egu´ıluz and Herna´ndez-Garc´ıa presents
an information-theoretic approach to derive a directed network of semantic flow
between Wikipedia articles using a complete snapshot of the English Wikipedia.
Theauthorsshowthattheresultingsemanticspaceischaracterizedbyascale-free
behavior at differentscales which implies a hierarchicalorganization of semantic
spaces.
VIII A.Mehleretal.
The chapter by Zweig confronts the physically-inspired context-free quest for
universal structures with the need of contextual interpretations in sociology and
in linguistics. Zweigquestionsthe usefulnessof networkrepresentationsofword-
adjacencyrelations,becausemostofthewell-knowntopologicalindicatorsrelyona
ratherspecificnetworkprocessandtheymaythereforebemisleadingifthisprocess
isnotknownornotadequatelymodeledbytheprocessunderlyingthemethod.
2.3 PartIII:Syntax
ThechapterbyCˇech,MacˇutekandLiupresentsacriticalreviewoftheapplication
of complex network tools to the analysis of syntax and points out the main chal-
lengesforfurtherresearch.Amongmanyotherthings,thearticlediscussestheim-
pactofsyntaxonnetworkproperties,thepreprocessingofdata,andtheapplication
ofnetworkstudiestolanguagetypologyandacquisition.
A second chapter dealing with syntactic dependency networks is by Chen and
Liu.Basedontwosyntacticdependencynetworksfromdifferentgenresthischapter
analyses the syntactic status of three function words in Chinese. The importance
(the authors propose the notion of syntactic centrality) of the words is analyzed
byindependentlyremovingthemfromthe networkandcomparingtheirstatistical
characteristicsbeforeandafterremoval.
ThechapterbyFerreriCanchochallengestheexistingtheoryofsyntaxbycon-
fronting the observation that syntactic dependencies between the words of a sen-
tence rarely cross when drawn over a sentence with two null hypotheses for the
expected number of crossings by chance. Relying on the trade-off between parsi-
monyandexplanatorypower,thechapterarguesthattheminimizationofsyntactic
dependencylength(asaprinciplethatderivesfromlimitedcomputationalresources
ofthebrain)canexplainuncrossingdependenciesandthatthisexplanationis,from
aneconomicpointofview,preferableoverexplanationsrelyingongrammar.
2.4 PartIV:Dynamics
Theroleofculturaltransmissioninlanguagechangeacrossthreegenerationsisan-
alyzed on the basis of an extended simulation model by Gong and Shuai. While
transmissionwithintheoffspringgenerationandbetweentheoffspringandthepar-
entgenerationfosterslanguagechangeand leads, at the same time, to mutualun-
derstandability within generations and across consecutive generations, interaction
betweenchildrenandtheirgrandparent’sgenerationplaysanimportantroleinpre-
servingmutualcross-generationalunderstandabilityinthelongrun.
AnothersimulationstudyispresentedbyBaxterwhocomplementshisnumerical
resultswithanalyticalarguments.Drawingonanevolutionaryapproachtolanguage
change,theauthorlooksindetailtotheconvergencebehaviorofthemodelondif-
ferent social networks and with heterogeneous patterns of mutual influence that,
takentogether,mayencodeavarietyofsocialstructures.
Introduction IX
The chapter by Maity and Mukherjee presents a simulation study of the effect
ofinflexibleindividualsonthedynamicsofthenaminggameandshowsthatrigid
minoritieslead to the emergenceof dominantstates in the population.The model
isanalyzedonaseriesofstaticnetworksofdifferentcomplexityrangingfromthe
completegraphtoscale-freetopologiesandadynamicnetworkobtainedfromreal-
worldtime-varyingface-to-faceinteractiondataisalsoconsidered.
2.5 PartV:Resources
Therequirementsofadataformatapplicabletothewiderangeoflinguisticnetwork
dataarediscussedinthechapterbyStu¨hrenberg,DiewaldandGleim.Theauthors
analyzevariousexistinggraphformatsinrelationtotheirexpressivityandsupport
bycommontoolsfornetworkanalysisandproposeanextensionofGraphMLasa
possiblycomplexdatamodelofa graphwhichallowstoquicklyextractviewsfor
specifictasks,ratherthanextractingincoherentdifferentviewsfromrawdata.Itis
noteworthy,thatthischaptergrewoutofaworkinggroupthatwasconstitutedatthe
MLNconference.
ThebookconcludeswiththechapterbyMehlerandGleimwhopresenttheLN
system, an online platform for the automatic generation of lexical networks from
texts.Itaddressestwocommunities:ontheonehandhumanitiesscholars(e.g.,his-
toricalsemanticists)whoaimatstudyingthechangeoflanguageuseasanindicator
of social-semantic change. On the other hand, network theorists who are in need
ofnullmodelsformakinglinguisticnetworkscomparable.TheworkflowoftheLN
system–usingGraphMLasanoutputstandardforlinguisticnetworks–isexplained
andexemplified.