Fine-Grained Entity Recognition Xiao Ling and Daniel S. Weld Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. fxiaoling,
[email protected] Abstract organization standard, they still fail to distinguish entity classes which are common when extracting hundreds or Entity Recognition (ER) is a key component of relation ex- traction systems and many other natural-language processing thousands of different relations (Hoffmann, Zhang, and applications. Unfortunately, most ER systems are restricted Weld 2010; Carlson et al. 2010). Furthermore, as one strives to produce labels from to a small set of entity classes, e.g., for finer granularity, their assumption of mutual exclusion person, organization, location or miscellaneous. In order to breaks (e.g., Monopoly is both a game and a product). intelligently understand text and extract a wide range of in- There are three challenges impeding the development of formation, it is useful to more precisely determine the se- a fine-grained entity recognizer: selection of the tag set, cre- mantic classes of entities mentioned in unstructured text. This ation of training data, and development of a fast and ac- paper defines a fine-grained set of 112 tags, formulates the curate multi-class labeling algorithm. We address the first tagging problem as multi-class, multi-label classification, de- scribes an unsupervised method for collecting training data, challenge by curating a set of 112 unique tags based on Free- and presents the FIGER implementation. Experiments show base types. The second challenge, creating a training sets for that the system accurately predicts the tags for entities.