In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4 birth cases from a set of 48 randomly picked samples and 57 split join cases from a set of 21 randomly picked samples. Remarkably, in 44 cases the birth of a novel sense is attested by WordNet, while in 46 cases and 43 cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.
The Japanese WordNet currently has 51,000 synsets with Japanese entries. In this paper, we discuss three methods of extending it: increasing the cover, linking it to examples in corpora and linking it to other resources (SUMO and GoiTaikei). In addition, we outline our plans to make it more useful by adding Japanese definition sentences to each synset. Finally, we discuss how releasing the corpus under an open license has led to the construction of interfaces in a variety of programming languages.  FinnWordNet is a Finnish wordnet which complies with the structure of the Princeton WordNet (Fellbaum, 1998). It was created by translating all the words in Princeton WordNet. It is open source and contains over 117 000 synsets. We are now testing different methods in order to improve and expand the content of FinnWordNet. Since wordnets are structured ontologies, a location for a word in FinnWordNet can be pinpointed by its relations to other words. To us, finding a location for a word therefore means finding a hyperonym, a hyponym or a synonym for the word. This article describes some methods for finding a location for a new word in FinnWordNet. Our methods include searching for multiword terms, compounds and lexicosyntactic patterns. Testing shows that with a few simple methods, we were able to find an indicator of the location for 83.2 of new words. Out of the new synonym pairs we tested, we were able to find an indication for 86.7 .