A Wordnet from the Ground Up

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3.4. Measures of Semantic Relatedness 61The results, as expected, are much lower than those achieved in WBST+H tests. Wewere surprised that the results the raters had for the frequent LUs were significantlylower than for all LUs. It is likely that more frequent lemmas are at same timemore polysemous, and that makes them more difficult to distinguish from other similarlemmas. The results for frequent LUs are lower, but at the level similar to the resultsfor all LUs.In 2008, in parallel with the new WBST+H versions, we generated a version ofEWBST (for nominal LUs) based on the May 2008 plWordNet version. It included80 LUs selected randomly in 4 groups of 20 LUs for each range of LU frequency inIPIC. Again, native speakers of Polish, mainly students of Computer Science, solvedthe tests via a dedicated Web page. The results and the number of raters are presentedin Table 3.5.Raters Min [%] Max [%] Avg [%]30 52.54 81.24 71.34Table 3.5: Results of human raters in EWBST (for nominal LUs) generated from the final version of thecore plWordNet3.4 Measures of Semantic Relatedness3.4.1 The distributional hypothesis and its consequencesHarris (1968) in his statement of the Distributional Hypothesis expressed a strongbelief that there is a direct relation between the observed use of language expressionsand their meaning (cited after to (Sahlgren, 2001)):The meaning of entities, and the meaning of grammatical relationsamong them, is related to the restriction on combinations of these entitiesrelative to other entities.Entities – language expressions or lemmas 6 (Section 1.2) that occur in text – interactvia complex syntactic and semantic relations. The occurrence in text of a particularlanguage expression is limited by constraints. Constraints are imposed by the cooccurringlanguage expressions and the instances of grammatical relations induced bythis coincidence. Each occurrence of a language expression can therefore be describedby a set of lexicalised constraints. Harris contends that two language expressions withsimilar sets of lexicalised constraints across their occurrences have a similar meaning.6 Lemmas are more convenient in the case of inflectional languages: they help reduce the complexitycaused by a large number of word forms.
62 Chapter 3. Discovering Semantic RelatednessLUs manifest themselves in text by occurrences of language expressions. That is whywe can also extend the application of this hypothesis to the meaning of LUs.The Distributional Hypothesis allows one to assess the commonalities between LUmeanings by measuring the similarity of contexts in which they occur (via languageexpressions). Grammatical relations are recognised in text mostly exact to some degreeof accuracy, so in the general case we should rather talk about measuring the strengthof semantic relatedness between LUs – not the exact semantic relations between them.The semantic relatedness that is a correlate of the likelihood that two LUs can occurin the same type of contexts.A Measure of Semantic Relatedness [MSR], briefly discussed in Section 3.2, isa function that assigns a real value to the semantic relatedness of two LUs by comparingthe descriptions of their distribution across different contexts in the corpus.High recall is an intrinsic property of an MSR. An MSR finds a value of the strengthof relatedness for almost any pair of LUs. Moreover, for a given LU x and a largeenough value of k one can expect many LUs related to x by one of the PWN relationsamong k LUs most semantically related to x – henceforth, we will denote this set ofLUs by MSRlist (x,k) . In practice, however, we mostly see a low accuracy of MSRsmeasured as the cut-off precision of the MSRlist (x,k) list calculated in comparison torelation instances extracted from a wordnet (Section 3.3) for a fixed value of k, suchas 20. See the result of the experiments later in this section. Nevertheless, despitethe expected problems with accuracy, but due to the expected high recall, our goalfor the first step of constructing tools for semi-automatic expansion of plWordNet wasto build an MSR for Polish with a relatively high accuracy with respect to the coreplWordNet. We planned to achieve this by working with a very large corpus – toincrease the number of examples of LU use – and by using rich description of contextsbased on the analysis of morphosyntactic dependencies among LU occurrences. Weexpected to extract an MSR more focused on semantic similarity, which returns a largepercentage of LUs associated with x by synonymy or hypernymy among MSRlist (x,k)for some LU x and a small value of k. The idea is to let the linguist browse the wholeMSRlist (x,k) comfortably. Preliminary experiments also suggested that linguists mightnot accept less than 50% of correct instances of lexico-semantic relations on the listof suggestions.3.4.2 Context and its descriptionThe construction of an MSR requires two decisions first: on the context size (orgranularity of the LU meaning description) and on the types of constraints used ascontext description. The decisions are correlated. For example, with context thatexceeds sentence boundaries, the description cannot be based only on lexico-syntacticrelations (most syntactic relations do not hold outside a sentence). Two main lines ofwork emerge in the literature – MSR extraction based on:
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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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112 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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