A Wordnet from the Ground Up

36 Chapter 2. Building a Wordnet Core2.4 The First 7000 Lexical UnitsThe plWordNet project depends crucially on an initial manually built small network.It has been our firm belief from the start that semi-automated construction of a wordnetfrom the ground up requires a completely trustworthy core to achieve acceptableaccuracy. We assumed that automated methods will not perform well for more generalLUs, and will be unable to extract the basic structure of the future plWordNet. The firstassumption turned out to be too pessimistic. Measures of Semantic Relatedness (Section3.4) produced results of lower accuracy only for some more general LUs, while forothers the results were quite correct or even good. The second assumption, however,has been borne out by the experiments with state-of-the-art clustering algorithms forthe extraction of synsets and possibly a hypernymy structure – see Section 3.5.We planned a fully manual construction 2 of core plWordNet with approximately7000 LUs. Those would be LUs with a general meaning, such as nouns located in theupper part of the hypernymy structure, including LUs which represent concepts suchas rzecz ‘thing’ or substancja ‘substance’. We had settled upon not translating anyexisting wordnet, and no monolingual dictionary in an electronic form was available tobe leveraged as a source of the plWordNet structure, especially the hypernymy structure.That is why we initially decided to rely only on a large enough corpus. The best choicefor Polish was the IPI PAN Corpus [IPIC] (Przepiórkowski, 2004) – the largest availablecorpus of Polish at the time when the project began. IPIC, designed as a corpus ofgeneral Polish, consists of about 254 million tokens and contains a range of genres,including literature, poetry, newspapers, scientific texts, legal texts and stenographicparliamentary records. It is not balanced: the last category dominates (Przepiórkowski,2006).We first extracted 10000 most frequent one-word lemmas 3 in IPIC 1.0, each taggedwith a grammatical class 4 (in the technical sense, see page 17, Section 1.2). Wecollected more lemmas than the planned size of the core plWordNet, because weexpected the list to shrink after manual revision. We divided them manually into 45general semantic domains (26 nominal, 15 verbal, 4 adjectival) corresponding to thedomains that label source files of Princeton WordNet [PWN] 1.5.Simultaneously with the grouping, the linguists filtered out typos and rare lemmaswhose high frequency was an artefact of errors in morphosyntactic tagging of IPIC 1.0.For example, the verb maić ‘≈ adorn with verdure’ normally occurs very rarely inrather old fashioned constructions (the use of any finite form is hard to imagine). The2 There was logistical software support for the process, but all lexicographic decision were to be madeby linguists.3 A method of extracting two-word lemmas was developed later.4 In the IPIC tagset, word forms are divided into 32 grammatical classes, a division more-fine grainedthan the traditional parts of speech.