Boosting Unsupervised Relation Extraction by Using NER

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 473–481, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Boosting Unsupervised Relation Extraction by Using NER Ronen Feldman Computer Science Department Bar-Ilan University Ramat-Gan, ISRAEL feldman@cs.biu.ac.il Benjamin Rosenfeld Computer Science Department Bar-Ilan University Ramat-Gan, ISRAEL grurgrur@gmail.com Abstract Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. URES is a Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target relations and their attributes. The performance of URES is further enhanced by classifying its output instances using the properties of the extracted patterns. The features we use for classification and the trained classification model are independent from the target relation, which we demonstrate in a series of experiments. In this paper we show how the introduction of a simple rule based NER can boost the performance of URES on a variety of relations. We also compare the performance of URES to the performance of the state- of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than URES. 1 Introduction Information Extraction (IE) (Riloff 1993; Cowie and Lehnert 1996; Grishman 1996; Grishman 1997; Kushmerick, Weld et al. 1997; Freitag 1998; Freitag and McCallum 1999; Soderland 1999) is the task of extracting factual assertions from text. Most IE systems rely on knowledge engineering or on machine learning to generate extraction patterns – the mechanism that extracts entities and relation instances from text. In the machine learning approach, a domain expert labels instances of the target relations in a set of documents. The system then learns extraction patterns, which can be applied to new documents automatically. Both approaches require substantial human effort, particularly when applied to the broad range of documents, entities, and relations on the Web. In order to minimize the manual effort necessary to build Web IE systems, we have designed and implemented URES (Unsupervised Relation Extraction System). URES takes as input the names of the target relations and the types of their arguments. It then uses a large set of unlabeled documents downloaded from the Web in order to learn the extraction patterns. URES is most closely related to the KnowItAll system developed at University of Washington by Oren Etzioni and colleagues (Etzioni, Cafarella et al. 2005), since both are unsupervised and both leverage relation- independent extraction patterns to automatically generate seeds, which are then fed into a pattern-learning component. KnowItAll is based on the observation that the Web corpus is highly redundant. Thus, its selective, high-precision extraction patterns readily ignore most sentences, and focus on sentences that indicate the presence …

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