Web page segmentation (WPS) aims to break a web page into different segments with coherent intra- and inter-semantics. By evidencing the morpho-dispositional semantics of a web page, WPS has traditionally been used to demarcate informative from non-informative content, but it has also evidenced its key role within the context of non-linear access to web information for visually impaired people. For that purpose, a great deal of ad hoc solutions have been proposed that rely on visual, logical, and/or text cues. However, such methodologies highly depend on manually tuned heuristics and are parameter-dependent. To overcome these drawbacks, principled frameworks have been proposed that provide the theoretical bases to achieve optimal solutions. However, existing methodologies only combine few discriminant features and do not define strategies to automatically select the optimal number of segments. In this article, we present a multi-objective clustering technique called MCS that relies on \( K \) -means, in which (1) visual, logical, and text cues are all combined in a early fusion manner and (2) an evolutionary process automatically discovers the optimal number of clusters (segments) as well as the correct positioning of seeds. As such, our proposal is parameter-free, combines many different modalities, does not depend on manually tuned heuristics, and can be run on any web page without any constraint. An exhaustive evaluation over two different tasks, where (1) the number of segments must be discovered or (2) the number of clusters is fixed with respect to the task at hand, shows that MCS drastically improves over most competitive and up-to-date algorithms for a wide variety of external and internal validation indices. In particular, results clearly evidence the impact of the visual and logical modalities towards segmentation performance.

Tag thunder is an audio version of a visual tag cloud content representation. Tag thunders aim to bring quick reading strategies, such as skimming, to blind people. Tag thunders vocalize the key terms of a page using concurrent speech paradigm coupled with additional audio effects, similar to visual effects in a tag cloud. In this paper we present our implementation of the tag thunder concept. Our system comprises three modules: page segmentation, key term extraction and tag thunder vocalization. The evaluation results show the viability of the tag thunder concept.

The ability to capture the time information conveyed in natural language is essential to many natural language processing applications such as information retrieval, question answering, automatic summarization, targeted marketing, loan repayment forecasting, and understanding economic patterns. In this paper, we propose a graph-based semi-supervised classification strategy that makes use of WordNet definitions or ‘glosses’, its conceptual-semantic and lexical relations to supplement WordNet entries with information on the temporality of its word senses. Intrinsic evaluation results show that the proposed approach outperforms prior semi-supervised, non-graph classification approaches to the temporality recognition of word senses, and confirm the soundness of the proposed approach.

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.

Mohammed Hasanuzzaman, Gaël Dias, Stéphane Ferrari, Yann Mathet. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. 2014.

International audience

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.