Abstract
Many current documents include multimedia consisting of text, images and embedded videos. This paper presents a general method that uses Random Forests to automatically extract keyphrases that can be used as very short summaries and to help in retrieval, classification and clustering processes.
Cite
CITATION STYLE
Hacohen-Kerner, Y., Vrochidis, S., Liparas, D., Moumtzidou, A., & Kompatsiaris, I. (2014). Keyphrase Extraction using Textual and Visual Features. In V and L Net 2014 - 3rd Annual Meeting of the EPSRC Network on Vision and Language and 1st Technical Meeting of the European Network on Integrating Vision and Language, A Workshop of the 25th International Conference on Computational Linguistics, COLING 2014 - Proceedings (pp. 121–123). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-5421
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