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Mining Complex Entities from Heterogeneous Information Networks

Mining Complex Entities from Heterogeneous Information Networks

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Most research on information mining has focused on classic Information Extraction (IE) tasks, from structured and unstructured documents, like newspaper articles and web pages. In the last years however the staggering growth of social media as platform for sharing content has moved the focus towards a different type of extraction target. Social media pose a number of challenge to information extraction: contributions to social media sites like blogs, forums, Twitter, etc. are conversational in nature and thus tend to be brief and informal, containing imprecise, subjective and ambiguous information. The expanded context (who the author is, the social and geographical context, their social links, etc.) becomes relevant to disambiguate and interlink information. Aim of this tutorial is to introduce and discuss issues, methodologies and technologies for extracting information from documents, with a particular focus on mining heterogeneous information networks (e.g. social websites) in order to mine complex entities. The tutorial covers: Introduction to information extraction from documents in general (20 minutes) and from information networks in particular (10 minutes) Introduction to machine learning based methods for information extraction (75 minutes) representing documents and feature sets entity and terminology recognition learning gazetteers event and relation extraction extraction from multimedia documents Annotation for training (15 minutes) feature selection annotation and error porting across domains Information Extraction from information networks (45 minutes) using the Twitter and Facebook APIs entity recognition and resolution term association entity disambiguation over large scale Conclusion and future work (15 minutes) The focus is on Machine Learning based methods. We will cover - among others - methods using Rule Induction, SVM, CRF, HMM, Transfer Learning, Active Learning. We will Also discuss real world cases from the field of information and knowledge management.

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