Toward General-Purpose Learning for Information Extraction

Toward General-Purpose Learning for Information Extraction Dayne Freitag School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA dayne©cs, crau. edu Abstract Two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broaden- ing of the central problem definition to include many non-traditional text domains. This devel- opment calls for information extraction systems which are as retctrgetable and general as possi- ble. Here, we describe SRV, a learning archi- tecture for information extraction which is de- signed for maximum generality and flexibility. SRV can exploit domain-specific information, including linguistic syntax and lexical informa- tion, in the form of features provided to the sys- tem explicitly as input for training. This pro- cess is illustrated using a domain created from Reuters corporate acquisitions articles. Fea- tures are derived from two general-purpose NLP systems, Sleator and Temperly's link grammar parser and Wordnet. Experiments compare the learner's performance with and without such linguistic information. Surprisingly, in many cases, the system performs as well without this information as with it. 1 Introduction The field of information extraction (IE) is con- cerned with using natural language processing (NLP) to extract essential details from text doc- uments automatically. While the problems of retrieval, routing, and filtering have received considerable attention through the years, IE is only now coming into its own as an information management sub-discipline. Progress in the field of IE has been away from general NLP systems, that must be tuned to work ill a particular domain, toward faster sys- tems that perform less linguistic processing of documents and can be more readily targeted at novel domains (e.g., (Appelt et al., 1993)). A natural part of this development has been the introduction of machine learning techniques to facilitate the domain engineering effort (Riloff, 1996; Soderland and Lehnert, 1994). Several researchers have reported IE systems which use machine learning at their core (Soder- land, 1996; Califf and Mooney, 1997). Rather than spend human effort tuning a system for an IE domain, it becomes possible to conceive of training it on a document sample. Aside from the obvious savings in human development ef- fort, this has significant implications for infor- mation extraction as a discipline: Retargetability Moving to a novel domain should no longer be a question of code mod- ification; at most some feature engineering should be required. Generality It should be possible to handle a much wider range of domains than previ- ously. In addition to domains characterized by grammatical prose, we should be able to perform information extraction in domains involving less traditional structure, such as netnews articles and Web pages. In this paper we describe a learning algorithm similar in spirit to FOIL (Quinlan, 1990), which takes as input a set of tagged documents, and a set of features that control generalization, and produces rules that describe how to extract in- formation from novel documents. For this sys- tem, introducing linguistic or any other …

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