A Wordnet from the Ground Up

1.2. The Goals of the plWordNet Project 17According to our initial plans, an extraction algorithm should suggest both newsynsets and instances of lexico-semantic relations. In the end, the WordNet Weaver generatesonly suggestions of attachment points (Section 4.5.3): synsets in which a givennew LU can be included or to which it can be attached as a new hyponym/hypernym oreven meronym. The accuracy of clustering-based methods of suggesting new synsetsended up too low for practical applications (Section 3.5). The use of support toolsnotwithstanding, we wanted to abide by the principle that the ultimate responsibilityfor every wordnet element rests with its authors in every phase of the wordnet development.It was tempting to speed up the development of our wordnet at the cost ofslightly lower accuracy, but we are convinced that a smaller wordnet with excellentaccuracy is more useful in applications than a larger but less reliable resource.Despite the limited funds, we fully expected to build a wordnet of a size comparableto several much better established European wordnets. The introduction ofthe automated methods in the second phase of the project was meant to reduce thelinguistic workload considerably 8 . Section 4.5.4 reports on the extent to which thissucceeded.There are many methods of extracting lexico-semantic relations from corpora. Wepresent an overview and a detailed discussion of selected methods throughout Chapters3 and 4. They can be roughly divided into two main groups of methods, basedondistribution (Chapter 3) and on patterns (Chapter 4). The former can achieve a relativelygood accuracy in extracting instances of hypernymy – pairs of LUs – but veryrarely of other relations such as synonymy, meronymy or antonymy; the recall is low.Distributional methods achieve good recall, because they can generate a descriptionfor any pair of LUs, but their accuracy is quite low: they do not distinguish betweendifferent lexico-semantic relations and produce a vague measure of semantic relatedness.A well-known weakness of distributional methods is in distinguishing different LUsfor the given lemma. Henceforth, we will understand lemma to be a basic morphologicalword form that represents the occurrences of one or a few particular LUs inlanguage expressions. A lemma is monosemous if it represents one LU, and polysemousotherwise. The basic morphological word form, or base form, is a word formor language expression with conventional values of grammatical categories, such asthe nominative case and singular number for nouns. A base form represents a set ofword forms with the same meaning and different values of grammatical categories. Wedecided to operate on lemmas during the extraction of relation instances, because thenumber of different word forms is very high in the strongly inflected Polish language.Lemmatisation, or the mapping of word forms to lemmas, must be done automatically8 That is why we have allotted the funds approximately in the proportion 1:2 to manual work and tothe software design and development work.