Open Language Learning for Information Extraction

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 523–534, Jeju Island, Korea, 12–14 July 2012. c⃝2012 Association for Computational Linguistics Open Language Learning for Information Extraction Mausam, Michael Schmitz, Robert Bart, Stephen Soderland, and Oren Etzioni Turing Center Department of Computer Science and Engineering University of Washington, Seattle {mausam,schmmd,rbart,soderlan,etzioni}@cs.washington.edu Abstract Open Information Extraction (IE) systems ex- tract relational tuples from text, without re- quiring a pre-specified vocabulary, by iden- tifying relation phrases and associated argu- ments in arbitrary sentences. However, state- of-the-art Open IE systems such as REVERB and WOE share two important weaknesses – (1) they extract only relations that are medi- ated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents OLLIE, a sub- stantially improved Open IE system that ad- dresses both these limitations. First, OLLIE achieves high yield by extracting relations me- diated by nouns, adjectives, and more. Sec- ond, a context-analysis step increases preci- sion by including contextual information from the sentence in the extractions. OLLIE obtains 2.7 times the area under precision-yield curve (AUC) compared to REVERB and 1.9 times the AUC of WOEparse. 1 Introduction While traditional Information Extraction (IE) (ARPA, 1991; ARPA, 1998) focused on identifying and extracting specific relations of interest, there has been great interest in scaling IE to a broader set of relations and to far larger corpora (Banko et al., 2007; Hoffmann et al., 2010; Mintz et al., 2009; Carlson et al., 2010; Fader et al., 2011). However, the requirement of having pre-specified relations of interest is a significant obstacle. Imagine an intel- ligence analyst who recently acquired a terrorist’s laptop or a news reader who wishes to keep abreast of important events. The substantial endeavor in 1. “After winning the Superbowl, the Saints are now the top dogs of the NFL.” O: (the Saints; win; the Superbowl) 2. “There are plenty of taxis available at Bali airport.” O: (taxis; be available at; Bali airport) 3. “Microsoft co-founder Bill Gates spoke at ...” O: (Bill Gates; be co-founder of; Microsoft) 4. “Early astronomers believed that the earth is the center of the universe.” R: (the earth; be the center of; the universe) W: (the earth; be; the center of the universe) O: ((the earth; be the center of; the universe) AttributedTo believe; Early astronomers) 5. “If he wins five key states, Romney will be elected President.” R,W: (Romney; will be elected; President) O: ((Romney; will be elected; President) ClausalModifier if; he wins five key states) Figure 1: OLLIE (O) has a wider syntactic range and finds extractions for the first three sentences where REVERB (R) and WOEparse (W) find none. For sentences #4,5, REVERB and WOEparse have an incorrect extraction by ignoring the context that OLLIE explicitly represents. analyzing their corpus is the discovery of important relations, which are likely not pre-specified. Open IE (Banko et al., 2007) is the state-of-the-art approach for such scenarios. However, the …

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