KeNet: A comprehensive Turkish wordnet and its applications in text clustering

In this thesis, we summarize the methodology and the results of our efforts to construct a comprehensive WordNet for Turkish. Most languages have access to comprehensive language resources. Traditional resources like bilingual dictionaries, monolingual dictionaries, thesauri, and lexicons are developed by lexicographers. As computer processing of languages gains popularity, a new set of resources become necessary. One such resource is WordNet which was initially constructed for the English language at Princeton University. A WordNet contains much of the information contained in a classic dictionary, but it also contains additional relationship information. These relations go beyond synonym relation and give information about relations such as a word being"is-a" or "is-a-part-of" another. These semantic relations are used in many text analysis tasks. A WordNet also categorizes words under common concepts. These concepts are called as synsets. As a result of all these, WordNet is a comprehensive dictionary which is readable by the computers and a useful language resource for text analysis and other research based on human language. In Turkish language, our WordNet is not the first. The previous WordNet is part of BalkaNet project which is a multilingual WordNet including Turkish and Balkan languages. BalkaNet contains only common words between these languages, as such BalkaNet does not contain all Turkish words and suffers from top-down constructing method disadvantages. BalkaNet project has not been updated or expanded in recent years. In this work, we construct a Turkish WordNet from scratch using a bottom-up method. In general, there are two methods for constructing WordNets. Bottom-up method means that we create the WordNet from scratch while top-down approach uses other WordNets by translating them. We use Turkish Contemporary Dictionary (CDT) which is an online Turkish dictionary provided by Turkish Language Institute. Bottom-up approach has its own difficulties since constructing a WordNet from scratch requires more resources and a lot of effort. In this work, we extract synonyms from CDT and ask experts to match common meanings for pairs of synonyms. We developed an application which makes annotation step easier and more accurate. We also use two groups of annotators to measure inter-annotator agreement. We used some automatic approaches to extract semantic relations from Turkish Wikipedia (Vikipedi) and Vikisözlük. We processed CDT to extract candidate synonyms and used rule-based approaches to find synonym sets. There is no thesaurus for Turkish, so as an application we construct a thesaurus automatically and measured accuracy with our manually constructed synsets. We named our WordNet "KeNet". Finally, in this thesis, we developed a novel approach to represent a text document in a vector space. This approach uses WordNet semantic relations. This part of the thesis is an application of KeNet. We used our approach to represent text documents and implemented two different clustering algorithms over these vectors. We tested our method over Turkish Wikipedia articles, domains of which are labeled by Wikipedia.