A Wordnet from the Ground Up

78 Chapter 3. Discovering Semantic Relatedness• and a corpus of large texts in Polish (about 214 million tokens) collected from theInternet; only documents containing a small percentage of erroneous word forms(tested manually) and not duplicated in the other two corpora were included inthe collected corpus.Henceforth, we will refer to all three corpora used together as the joint corpus.For nominal MSR, fours types of lexico-morphosyntactic constraints have beenused (Section 3.4.3): AdjC, NcC, NmgC and VsbC. Lists of lexical elements have beendefined as the combination of one-word LUs in the joint corpus and two-word LUsassumed for the expansion of plWordNet. We also used 63328 adjectives and adjectivalparticiples for AdjC, 199250 one-word and two-word nominal LUs for NcC and NmgC,and 29564 verbs for VsbC.Evaluation of the extracted nominal MSRs was performed on the basis of theWBST+H and EWBST tests presented in Section 3.3. The WBST+H test used forthe final version of the nominal MSR, generated from the plWordNet version 11.2008consisted of 9486 questions; EWBST had 8689. Table 3.9 shows the results of theWBST+H and EWBST. Table 3.10 includes the results in relation to particular types ofconstraints (we constructed several coincidence matrices, from which different MSRswere built and tested).all LUs more frequent than ≥ 1000WBST+H EWBST WBST+H EWBST88.14 69.75 92.28 75.43Table 3.9: The accuracy of the nominal MSR based on the generalised RWF and Lin’s version of MIAdjC NcC NmgC VsbC all≥ 10 3 all ≥ 10 3 all ≥ 10 3 all ≥ 10 3 all ≥ 10 3 all90.90 84.98 88.81 80.67 76.25 65.10 79.17 65.89 92.28 88.14Table 3.10: The accuracy [%] of nominal MSRs based on different morphosyntactic constraints; all MSRsuse Generalised RWF based on Lin’s MI. “≥ 10 3 ” means more frequent than 1000The best results achieved for the nominal MSR in WBST+H and EWBST (Table3.9) are close to the average human results: 86.64% and 71.34%, respectively(Section 3.3). Both tests can clearly be interpreted from the perspective of practicalapplication of the MSR. It can distinguish among semantically related and unrelatedLUs with the accuracy 88.14% (WBST+H) and semantically closely related and moreremotely related with accuracy 69.75% (EWBST). Moreover, our comparison of theMSR GRW F with several other MSRs based on methods proposed in the literature