12 January 2012 Web entity extraction based on entity attribute classification
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Abstract
The large amount of entity data are continuously published on web pages. Extracting these entities automatically for further application is very significant. Rule-based entity extraction method yields promising result, however, it is labor-intensive and hard to be scalable. The paper proposes a web entity extraction method based on entity attribute classification, which can avoid manual annotation of samples. First, web pages are segmented into different blocks by algorithm Vision-based Page Segmentation (VIPS), and a binary classifier LibSVM is trained to retrieve the candidate blocks which contain the entity contents. Second, the candidate blocks are partitioned into candidate items, and the classifiers using LibSVM are performed for the attributes annotation of the items and then the annotation results are aggregated into an entity. Results show that the proposed method performs well to extract agricultural supply and demand entities from web pages.
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Chuan-Xi Li, Chuan-Xi Li, Peng Chen, Peng Chen, Ru-Jing Wang, Ru-Jing Wang, Ya-Ru Su, Ya-Ru Su, } "Web entity extraction based on entity attribute classification", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 835014 (12 January 2012); doi: 10.1117/12.920237; https://doi.org/10.1117/12.920237
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