A Wordnet from the Ground Up

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4.4. Benefits of Extracted Patterns 125one pattern is required for stable behaviour of Estratto. The number of the preservedinstances depend on the threshold, it cannot be too high.Considering case 3, the results of the experiments presented in (Kurc and Piasecki,2008) suggested that lowering k increases precision. This is only partially true. A morethorough evaluation performed in (Kurc, 2008), see the last three rows of Table 4.3,revealed a more complex picture. The value of k should be kept small due to thestability of a model in which a small group of elite patterns generates semantic relations.It should be too small either, in order to allow for the exploration of the instance spaceby a richer set of patterns. The result obtained for k = 8 is the best outcome producedby Estratto. The manually assessed precision is high in comparison to the other resultsand, at the same time, the relative recall is very high: many hypernymy instances havebeen extracted.The data for case 4 are not presented in Table 4.3, but some experiments haveshown that infrequent patterns (< 4 occurrences in IPIC) should be filtered out beforegeneralization, because they introduce additional noise which causes good patterns tobe evaluated as worse (Kurc, 2008, Kurc and Piasecki, 2008).In case 5, the data show that PMI is much better than the z-score and t-scorestatistical measures of association (patterns versus instances) in the extraction of lexicosemanticrelations. T-score results are especially disappointing. It might be due to theinsufficient statistical evidence (the algorithm very often accepted instances which occuronly once).The third and the last group of experiments was prepared to check the ability of ESTand ESP to use a different corpus and extract relations other than hyponymy/hypernymy.The experiments showed that both EST and ESP can be successfully applied todifferent corpora. It seems, though, that each time the corpus is changed, a newconfidence threshold must be somehow discovered. For IPIC, the threshold valuewas 2.6 but for the Rzeczpospolita corpus we have found 0.9 to work fine, cf (Kurcand Piasecki, 2008). Tests performed on the corpus of large documents from theInternet appeared to be unsuccessful. This is a rather special case, since most of thetexts in this corpus are written in a literary style, so the language expressions aremore complex. One should also expect fewer defining sentences than in utility texts.It seems that this kind of corpus requires more powerful patterns to catch syntacticdependencies. The precision of Estratto-based patterns on this corpus remains inan interesting contrast with the results for the manually created patterns (Table 4.1).Those results are better for the Internet-based corpus than for IPIC. We note thatmanual patterns use more expressive description of the morphosyntactic associationsthan Estratto patterns. The further development of Estratto should go in this direction.Presently, the manual patterns seem to be a valuable extension of the automaticallyextracted patterns.
126 Chapter 4. Extracting Relation InstancesThe application of EST to different relation types appeared to be only partially successful.Tests on meronymy ended with the estimated precision below 30%, cf (Kurc,2008). We see three main reasons of this failure. The expressive power of the patternsis too low and some important morpho-syntactic dependencies are missed. Meronymyis actually a set of quite varied sub-relations: it could be reasonable to try to extracteach sub-relation separately. Finally, the trials ran on only one corpus.On the other hand, initial experiments on extracting antonymy (only for adjectives)gave promising results. The human-judged cut-off precision reached 39%. Still, fromthe point of view of plWordNet expansion, extracting meronymy and antonymy requiresfurther investigation.Figure 4.5 shows examples of seeds and hypernymy instances extracted from IPICby Estratto, version EST+nm. Here we list examples of patterns extracted from IPICand used in the extraction of the instances that appear in Figure 4.5.occ=31 rel=0.26803(hypo:subst:nom) być (hyper:subst:inst)(hypo:subst:nom) is/are (hyper:subst:inst)occ=20 rel=0.222222(hypo:subst:nom) i inny (hyper:subst:nom)(hypo:subst:nom) and other (hyper:subst:nom)occ=26 rel=0.103449(hypo:subst:inst) a inny (hyper:base:inst)(hypo:subst:inst) but other (hyper:base:inst)occ=15 rel=0.0684905(hypo:subst:inst) przypominać (hyper:subst:acc)(hypo:subst:inst) resemble (hyper:subst:acc)occ=41 rel=0.0263854(hypo:subst:loc) i w inny (hper:subst:loc)(hypo:subst:loc) and in other (hper:subst:loc)occ=86 rel=0.00708506(hypo:subst:nom) stać się (hyper:subst:inst)(hypo:subst:nom) become (hyper:subst:inst)
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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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1.2. The Goals of the plWordNet Pro
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1.2. The Goals of the plWordNet Pro
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1.3. Early Decisions 19Merge Model:
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1.3. Early Decisions 214. On the ot
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24 Chapter 2. Building a Wordnet Co
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48 Chapter 3. Discovering Semantic
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176 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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