Efficient document filtering using vector space topic expansion and pattern-mining
Comunicació presentada a: CIKM '17 the 2017 ACM on Conference on Information and Knowledge Management, celebrat del 6 al 10 de novembre de 2017 a Singapur, Singapur. ; Automatically extracting information from social media is challenging given that social content is often noisy, ambiguous, and inconsistent. However, as many stories break on social channels first before being picked up by mainstream media, developing methods to better handle social content is of utmost importance. In this paper, we propose a robust and effective approach to automatically identify microposts related to a specific topic defined by a small sample of reference documents. Our framework extracts clusters of semantically similar microposts that overlap with the reference documents, by extracting combinations of key features that de ne those clusters through frequent pattern mining. Thiis allows us to construct compact and interpretable representations of the topic, dramatically decreasing the computational burden compared to classical clustering and k-NN-based machine learning techniques and producing highly-competitive results even with small training sets (less than 1'000 training objects). Our method is eficient and scales gracefully with large sets of incoming microposts. We experimentally validate our approach on a large corpus of over 60M microposts, showing that it significantly outperforms state-of-theart techniques. ; J.P. was supported by a Sinergia Grant by the Swiss National Science Foundation (SNF 147609). C.C. worked on this paper while at Eurecat, supported by the Catalonia Trade and Investment Agency (ACCIÓ). P.C.M. was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 683253/GraphInt).
