Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering
Autor: | Srivatsa Ramesh Jayashree, Gaël Dias, Judith Jeyafreeda Andrew, Sriparna Saha, Fabrice Maurel, Stéphane Ferrari |
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Přispěvatelé: | Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Indian Institute of Technology Patna (IITP), Centre de recherche inter-langues sur la signification en contexte (CRISCO), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU) |
Rok vydání: | 2022 |
Předmět: |
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] [INFO.INFO-WB]Computer Science [cs]/Web [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] General Business Management and Accounting ComputingMilieux_MISCELLANEOUS Computer Science Applications Information Systems |
Zdroj: | ACM Transactions on Information Systems ACM Transactions on Information Systems, Association for Computing Machinery, 2021 |
ISSN: | 1558-2868 1046-8188 |
DOI: | 10.1145/3480966 |
Popis: | 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. |
Databáze: | OpenAIRE |
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