Speaker: Alan Akbik
Technische Unviersity, Berlin
Current techniques for Open Information Extraction (OIE) focus on the extraction of binary facts and suffer significant quality loss for the task of extracting higher order N-ary facts. This quality loss may not only affect the correctness, but also the completeness of an extracted fact. We present KrakeN, an OIE system specifically designed to capture N-ary facts, as well as the results of an experimental study on extracting facts from Web text in which we examine the issue of fact completeness. Our preliminary experiments indicate that KrakeN is a high precision OIE approach that captures more facts per sentence at greater completeness than existing OIE approaches, but is vulnerable to noisy and ungrammatical text.
Alan Akbik joined the DIMA-team in April 2011 as a research associate for the SCAPE project. His foci are scalable Information Extraction technologies, with the ultimate goal of mining large amounts of knowledge from large bodies of text. He received his master degree at the Freie Universität Berlin in 2009, where he designed and implemented an Open Information Extraction system using deep linguistic patterns . Prior to joining the DIMA-team, he worked at Neofonie GmbH as a research engineer and team leader, working on problems such as Named Entity Recognition, Coreference Resolution and Near-Duplicate Detection. His interests include – but are not limited to - Natural Language Processing, Computational Linguistics and Machine Learning technologies.