REES: A Large-Scale Relation and Event Extraction System

REES: A Large-Scale Relation and Event Extraction System Chinatsu Aone SRA International, Inc. 4300 Fair Lakes Court Fairfax, VA 22033 aonec@verdi.sra.com Mila Ramos-Santacruz SRA International, Inc. 4300 Fair Lakes Court Fairfax, VA 22033 mila@verdi.sra.com Abstract This paper reports on a large-scale, end-to- end relation and event extraction system. At present, the system extracts a total of 100 types of relations and events, which represents a much wider coverage than is typical of extraction systems. The system consists of three specialized pattem-based tagging modules, a high-precision co- reference resolution module, and a configurable template generation module. We report quantitative evaluation results, analyze the results in detail, and discuss future directions. Introduction One major goal of information extraction (IE) technology is to help users quickly identify a variety of relations and events and their key players in a large volume of documents. In contrast with this goal, state-of-the-art information extraction systems, as shown in the various Message Understanding Conferences (MUCs), extract a small number of relations and events. For instance, the most recent MUC, MUC-7, called for the extraction of 3 relations (person-employer, maker-product, and organization-location) and 1 event (spacecraft launches). Our goal is to develop an IE system which scales up to extract as many types of relations and events as possible with a minimum amount of porting effort combined with high accuracy. Currently, REES handles 100 types of relations and events, and it does so in a modular, configurable, and scalable manner. Below, Section 1 presents the ontologies of relations and events that we have developed. Section 2 describes REES' system architecture. Section 3 evaluates the system's performance, and offers a qualitative analysis of system errors. Section 4 discusses future directions. 1 Relation and Event Ontologies As the first step in building a large-scale relation and event extraction system, we developed ontologies of the relations and events to be extracted. These ontologies represent a wide variety of domains: political, financial, business, military, and life-related events and relations. "Relations" covers what in MUC-7 are called Template Elements (TEs) and Template Relations (TRs). There are 39 types of relations. While MUC TE's only dealt with singular entities, REES extracts both singular and plural entities (e.g., "five executives"). The TR relations are shown in italic in the table below. Relations Place Relations 'Artifact Relations Place-Name&Aliases Place-Type Place-Subtype Place-Descriptor Place-Country Artifact-Name&Aliases Artifact-Type Artifact-Subtype Artifact-Descriptor Artifact-Maker Artifact-Owner Organization Relations Person Relations Org-Name&Aliases Org-Descriptor Org-FoundationDate Org-Nationality Org-TickerSymbol Org-Location Org-P arentOrg Org-Owner Org-Founder Org-StockMarket Person-Name&Aliases Person-Type Person-Subtype Person-Descriptor Person-Honorific Person-Age Person-PhoneNumber Person-Nationality Person-Affiliation Person-Sibling Person-Spouse Person-Parent Person-Grandparent 76 Person-OtherRelative Person-BirthPlace Person-BirthDate Table 1: Relation Ontology "Events" are extracted along with their event participants, e.g., "who did what to whom when and where?" For example, for a BUYING event, REES extracts the buyer, the artifact, the seller, and the time and location of the BUYING event. REES currently covers 61 types of events, as shown below. Events Vehicle Transaction Vehicle departs Vehicle arrives Spacecraft launch Vehicle crash Personnel Change Hire Terminate contract Promote Succeed Start office Buy artifact …

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