A Wordnet from the Ground Up

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: