No abstract

Understanding the temporal orientation of web search queries is an important issue for the success of information access systems. In this paper, we propose a multi-objective ensemble learning solution that (1) allows to accurately classify queries along their temporal intent and (2) identifies a set of performing solutions thus offering a wide range of possible applications. Experiments show that correct representation of the problem can lead to great classification improvements when compared to recent state-of-the-art solutions and baseline ensemble techniques.

Web page segmentation aims to break a large page into smaller blocks, in which contents with coherent semantics are kept together. Within this context, a great deal of approaches have been proposed without any specific end task in mind. In this paper, we study different segmentation strategies for the task of non visual skimming. For that purpose, we propose to segment web pages into visually coherent zones so that each zone can be represented by a set of relevant keywords that can be further synthesized into concurrent speech. As a consequence, we consider web page segmentation as a clustering problem of visual elements, where (1) a fixed number of clusters must be discovered, (2) the elements of a cluster should be visually connected and (3) all visual elements must be clustered. Therefore, we study variations of three existing algorithms, that comply to these constraints: K-means, F-K-means, and Guided Expansion. In particular, we evaluate different reading strategies for the positioning of the initial K seeds as well as a pre-clustering methodology for the Guided Expansion algorithm, which goal is to (1) fasten the clustering process and (2) reduce unbalance between clusters. The performed evaluation shows that the Guided Expansion algorithm evidences statistically increased results over the two other algorithms with the variations of the reading strategies. Nevertheless, improvements still need to be proposed to increase separateness.

In this paper, we propose a textual clue approach to help metaphor detection, in order to improve the semantic processing of this figure. The previous works in the domain studied the semantic regularities only, overlooking an obvious set of regularities. A corpus-based analysis shows the existence of surface regularities related to metaphors. These clues can be characterized by syntactic structures and lexical markers. We present an object oriented model for representing the textual clues that were found. This representation is designed to help the choice of a semantic processing, in terms of possible non-literal meanings. A prototype implementing this model is currently under development, within an incremental approach allowing step-by-step evaluations. 1