Database and Expert Systems Applications (DEXA), 2010 Workshop on

This paper proposes to enhance search query log analysis by taking into account the semantic properties of query terms. We first describe a method for extracting a global semantic representation of a search query log and then show how we can use it to semantically extract the user interests. The global representation is composed of a taxonomy that organizes query terms based on generalization/specialization (“is a”) semantic relations and of a function to measure the semantic distance between terms. We then define a query terms clustering algorithm that is applied to the log representation to extract user interests. The evaluation has been done on large real-life logs of a popular search engine. View full abstract»

In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have been imposed. Due to the changing information need, automatic methods are needed to support blog search users to filter information by different facets. In our work, we aim to support blog search with genre and facet information. Since we focus on the news genre, our approach is to classify blogs into news versus rest. Also, we assess the emotionality facet in news related blogs to enable users to identify people's feelings towards specific events. Our approach is to evaluate the performance of text classifiers with lexical and stylometric features to determine the best performing combination for our tasks. Our experiments on a subset of the TREC Blogs08 dataset reveal that classifiers trained on lexical features perform consistently better than classifiers trained on the best stylometric features. View full abstract»