A Wordnet from the Ground Up

64 Chapter 3. Discovering Semantic Relatednessbroader semantic associations among LUs 9 . Such contexts tend to extract semanticrelatedness sensu largo rather than (more desirable) closer semantic similarity.In approaches based on lexico-syntactic constraints, a target LU is described byinstances of its lexico-syntactic relations with particular LUs. As an example, for thenoun bird we find the constraint subject of(sing ) met in texts. Hindle (1990)used a deterministic parser and analysed relations of nouns with verbs as subjects andobjects. Two measures, subject similarity and object similarity of two nouns in relationto a given verb, were calculated from the collected frequencies. The final MSR valuefor a pair of nouns was defined as a sum of both similarities across all verbs. Indefining MSR for 26742 nouns, Hindle used only 4789 verbs for which at least onesentence or clause structure (274613 in total) was recognised by the parser. Lexicosyntacticconstraints were applied for the construction of MSRs also by Ruge (1992),Grefenstette (1993), Widdows (2004), Weeds and Weir (2005).Lin (1998) applied a shallow dependency parser, MiniPar (Lin, 1993), to the preprocessingand identification of syntactic dependencies that involve nouns. The numberof different syntactic relation utilised for the MSR computation is not given; MiniParrecognises several hundred syntactic dependency relations, about 200 of which describedependency links involving noun phrase heads. Examples in (Lin, 1998) suggest thatmany different relation were used in defining lexico-syntactic constraints. The correlationof the MSRlist (x,k) list generated from Lin’s MSR with the MSRlist (x,k) listgenerated on the PWN-based similarity appeared to be much higher than the correlationwith lists generated from the MSR proposed in (Hindle, 1990). The result showedthat the use of a large set of syntactic dependencies, not only based on the subject andobject relations, improves the MSR.In the experiments on Polish data, we observed progress in WBST+H with theaddition of constraints of different types. For example, here are the observationsin the experiments performed for (Piasecki et al., 2007b): while the MSRs basedon the individual constraints expressing only adjectival modification and noun coordinationachieve 88.65% and 76.85%, respectively, an MSR based on the combinationof both constraints achieves 90.92% in WBST+H. We also made a comparison of MSRsconstructed as described in (Piasecki and Broda, 2007):• LSA applied to a subcorpus of the IPI PAN Corpus [IPIC] (Przepiórkowski,2004) including 185066 documents from a daily Polish newspaper – 58.07% inWBST+H generated from the core plWordNet,9 The same tendency could be observed in the similar experiments performed on a corpus of 584million token (the joint corpus, Section 3.4.5 and plWordNet from November 2008. We compared twoMSRs extracted for nominal LUs (13285, described in Section 3.4.5): one based on lexico-syntacticconstraints and another on pure co-occurrence in the text window of ±5 tokens. The results achieved inWBST+H test, 88.14% and 75.20% respectively, and 67.95% and 58.86% in EWBST, seem to supportthe claim that the use of text-window contexts results in less precise discrimination of LU meanings.