Kannada WordNet - a lexical database

This paper presents the preliminary analysis of Kannada WordNet and the set of relevant computational tools. Although the design has been inspired by the famous English WordNet, and to certain extent, by the Hindi WordNet, the unique features of Kannada WordNet are graded antonyms and meronymy relationships, nominal as well as verbal compoundings, complex verb constructions and efficient underlying database design (designed to handle storage and display of Kannada unicode characters). Kannada WordNet would not only add to the sparse collection of machine-readable Kannada dictionaries, but also will give new insights into the Kannada vocabulary. It provides sufficient interface for applications involved in Kannada machine translation, spell checker and semantic analyser.

Method of Design Through Structuring of Meanings

This research shows a new approach and development of a design methodology, based on the perspective of meanings. In this study the design process is explored as a development of the structure of meanings. The processes of search and evaluation of meanings form the foundations of developing this structure. In order to facilitate the use and operation of the meanings, the WordNet lexical database and an existing visualization of WordNet — Visuwords — is used for the process of meaning search. The basic tool used for evaluation process is the WordNet::Similarity software, measuring the relatedness of meanings in the database. In this way it is measuring the degree of interconnections between different meanings. This kind of search and evaluation techniques are later on incorporated into our methodology of the structure of meanings to support the design process. The measures of relatedness of meanings are developed as convergence criteria for application in the processes of evaluation. Further on, the methodology for the structure of meanings developed here is used to construct meanings in a verification of product design. The steps of the design methodology, including the search and evaluation processes involved in developing the structure of the meanings, are elucidated. The choices, made by the designer in terms of meanings are supported by consequent searches and evaluations of meanings to be implemented in the designed product. In conclusion, the paper presents directions for developing and further extensions of the proposed design methodology.

Methods for Automatic Evaluation of Sentence Extract Summaries

This paper describes three novel techniques to automatically evaluate sentence extract summaries. Two of these techniques called FuSE and DeFuSE evaluate the quality of the generated extract summary based on the degree of similarity to the model summary. They use a fuzzy set theoretic basis to generate a match score. DeFuSE is an enhancement to FuSE and uses WordNet based hypernymy structures to detect similarity between sentences at abstracted levels. The third technique focuses on quantifying the quality of an extract summary based on the difficulty in generating such a summary. Advantages of these techniques are described with examples.

Scalable multi-dimensional user intent identification using tree structured distributions

The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.