A Wordnet from the Ground Up

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3.4. Measures of Semantic Relatedness 65• co-occurrence with adjectives in a very small text-window (±2) – 74.16% in thesame WBST+H,• and morpho-syntactic constraints describing modification by adjective (Section3.4.3) – 81.15%.The experiments showed that an MSR for Polish which takes into account syntacticrelations, even limited to adjectival modification, more accurately differences betweensemantically similar and dissimilar nouns. So, an MSR based on lexico-syntacticconstraints is more useful for wordnet expansion than an MSR based on text windows.There is, however, an obvious drawback in the premises of this argumentation: thecorpus used in the first case was much smaller than the whole IPIC used in theother two experiments. Still, it is hard to predict what would happen, because thefirst experiment explored the limits of technical possibilities of SVD – we could notprocess any larger matrix. Motivated by the result of the third experiment, we decidedto focus on the constraint-based MSRs.Many methods have been proposed for MSR extraction, but they all contain fourgeneral steps, more or less clearly delineated.1. Corpus preprocessing – typically up to the level of shallow syntactic analysis.2. Co-incidence matrix construction – in which rows correspond to LUs beingdescribed and columns to features.3. Matrix transformation – a possible reduction of size and/or combination of featureweighting and selection.4. Semantic relatedness calculation – LU descriptions are compared by the applicationof the assumed measure of similarity between row vectors.The following sections discuss the steps and the corresponding results for Polish.3.4.3 Preprocessing based on morphosyntactic constraintsLandauer and Dumais (1997) used word forms collected from a corpus as elementsdescribed by MSR(LSA). Such a strategy is doomed to failure in the case of an inflectionallanguage like Polish – there are too many word forms and word forms of thesame lexeme have the same meaning but can have different distributions. A naturalstrategy would be to transform all word forms into their basic morphological wordforms. There is, however, much ambiguity. For example, the word form mam canrepresent three different lexemes with three different base forms:
66 Chapter 3. Discovering Semantic Relatedness1. mama (mam = mom case=gen,num=pl ),2. mieć (mam = have (possess) person=1st,num=sg,tense=present ),3. mamić (mam = delude imperative ).In order to disambiguate base form assignment, we applied the morphosyntactic taggerTaKIPI (Piasecki and Godlewski, 2006). The accuracy of the base form identificationby TaKIPI is 99.31% (Piasecki and Radziszewski, 2009), as measured in relation tothe manually disambiguated part of IPIC.MSR extraction methods based on lexico-syntactic constraints assume that thecorpus has been preprocessed by a parser. There is no available parser or shallowparser for Polish, which could be used for this task: Swigra (Woliński, 2005) is a deepparser that produce many possible detailed analyses for a sentence, the dependencyparser of Obrębski (2002) also returns several analyses for a sentence, and the Polengparser (Graliński, 2005) is a commercial product, whose version available for theplWordNet project caused problems with interpreting the output format 10 .Faced with the lack of a suitable parser, we considered the morphological informationencoded by Polish word forms. It has turned out to be rich enough for use ina tool to replace a parser. Lexico-morphosyntactic constraints as context descriptorshelp identify semantically relevant association between a target LU and other LUs inthe lexicon. In Polish, associations among language expressions very often depend onthe morphosyntactic characteristics of their constituents, such as gender/number/caseagreement between an adjective and a head noun. In an inflectional language like Polish,the morphosyntactic description of word forms (rather than word order) deliversmost of the structural information. For example, an adjective and a noun which areconstituents of the same noun phrase can occur in both possible orders 11 but the agreementis necessary. Morphosyntactic associations are also simpler to recognise, sincethis requires only a tagger and a constraint representation formalism. Morphosyntactictaggers have been created for most European languages; in our experience, a constraintlanguage interpreter can be constructed for a given language in a few person-weeks.The JOSKIPI language, originally introduced as the language of tagging rules inTaKIPI, was used to implement morphosyntactic constraints. Selected elements ofJOSKIPI will be presented as we discuss the constraint examples later in this section.For a detailed description, see (Piasecki, 2006, Piasecki and Radziszewski, 2009). Ingeneral, the expressions are used to recognise potential associations between a targetLU occurrence and occurrences of other LU in the given sentence. Each constraintis based on a template that has a marked place for a LU – a lexical element. A setof concrete constraints is generated from a list of lexical elements predefined for the10 It was designed as an internal module of a Machine Translation system.11 Except some fixed collocations.
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A Wordnetfrom the Ground Up
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Work financed by the Polish Ministr
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6 Prefaceheartfelt thanks go to all
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8 Chapter 1. Motivation, Goals, Ear
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1.1. Motivation 11[a] special form
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1.1. Motivation 13Affect (Strappara
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116 Chapter 4. Extracting Relation
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166 Chapter 5. Polish WordNet Today
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Appendix ATests for Lexico-semantic
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187Test for adjectives (T. IX)1. p1
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189RelatednessTest for nouns (T. XV
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BibliographyAgarwal, Abhaya and Alo
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Bibliography 193on Deep Lexical Acq
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Bibliography 195Derwojedowa, Magdal
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Bibliography 197Grefenstette, Grego
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Bibliography 199Kurc, Roman. (2008)
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Bibliography 201Mohammad, Saif and
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Bibliography 203. (2006) “The pot
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Bibliography 205and Technology 7(1-
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List of Tables2.1 The size of the c
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List of Figures2.1 The LU perspecti
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List of Figures 2114.16 Completely
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Index 213CBC, see Clustering by Com
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Index 215169, 177, 178, 180, 182hyp
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Index 217mutual hypernymy, 24Mutual
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Index 219SUMO, 14Supported Vector M
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A language without a wordnet is at
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A Wordnet from the Ground Up

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