Using Predicate-Argument Structures for Information Extraction

Using Predicate-Argument Structures for Information Extraction Mihai Surdeanu and Sanda Harabagiu and John Williams and Paul Aarseth Language Computer Corp. Richardson, Texas 75080, USA mihai,sanda@languagecomputer.com Abstract In this paper we present a novel, cus- tomizable IE paradigm that takes advan- tage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument struc- tures enable high quality IE results. 1 Introduction The goal of recent Information Extraction (IE) tasks was to provide event-level indexing into news stories, including news wire, radio and television sources. In this context, the purpose of the HUB Event-99 evaluations (Hirschman et al., 1999) was to capture information on some newsworthy classes of events, e.g. natural disasters, deaths, bombings, elections, financial fluctuations or illness outbreaks. The identification and selective extraction of rele- vant information is dictated by templettes. Event templettes are frame-like structures with slots rep- resenting the event basic information, such as main event participants, event outcome, time and loca- tion. For each type of event, a separate templette is defined. The slots fills consist of excerpts from text with pointers back into the original source mate- rial. Templettes are designed to support event-based browsing and search. Figure 1 illustrates a templette defined for “market changes” as well as the source of the slot fillers. <MARKET_CHANGE_PRI199804281700.1717−1>:= CURRENT_VALUE: $308.45 LOCATION: London DATE: daily INSTRUMENT: London [gold] AMOUNT_CHANGE: fell [$4.70] cents London gold fell $4.70 cents to $308.35 Time for our daily market report from NASDAQ. Figure 1: Templette filled with information about a market change event. To date, some of the most successful IE tech- niques are built around a set of domain relevant lin- guistic patterns based on select verbs (e.g. fall, gain or lose for the “market change” topic). These pat- terns are matched against documents for identifying and extracting domain-relevant information. Such patterns are either handcrafted or acquired automat- ically. A rich literature covers methods of automati- cally acquiring IE patterns. Some of the most recent methods were reported in (Riloff, 1996; Yangarber et al., 2000). To process texts efficiently and fast, domain pat- terns are ideally implemented as finite state au- tomata (FSAs), a methodology pioneered in the FASTUS IE system (Hobbs et al., 1997). Although this paradigm is simple and elegant, it has the dis- advantage that it is not easily portable from one do- main of interest to the next. In contrast, a new, truly domain-independent IE paradigm may be designed if we know (a) predicates relevant to a domain; and (b) which of their argu- ments fill templette slots. Central to this new way of extracting information from texts are systems that label predicate-argument structures on the output of full parsers. One such augmented parser, trained on data available from the PropBank project has been recently presented in (Gildea and Palmer, 2002). In …

Content truncated to 500 words