Unsupervised Relation Extraction From Web Documents

Unsupervised Relation Extraction from Web Documents Kathrin Eichler, Holmer Hemsen and G¨unter Neumann DFKI GmbH, LT-Lab, Stuhlsatzenhausweg 3 (Building D3 2), D-66123 Saarbr¨ucken {FirstName.SecondName}@dfki.de Abstract The IDEX system is a prototype of an interactive dynamic Information Extraction (IE) system. A user of the system expresses an information request in the form of a topic description, which is used for an initial search in order to retrieve a relevant set of documents. On basis of this set of documents, unsupervised relation extraction and clustering is done by the system. The results of these operations can then be interactively inspected by the user. In this paper we describe the relation extraction and clustering components of the IDEX system. Preliminary evaluation results of these components are presented and an overview is given of possible enhancements to improve the relation extraction and clustering components. 1. Introduction Information extraction (IE) involves the process of au- tomatically identifying instances of certain relations of interest, e.g., produce(<company>, <product>, <lo- cation>), in some document collection and the con- struction of a database with information about each individual instance (e.g., the participants of a meet- ing, the date and time of the meeting). Currently, IE systems are usually domain-dependent and adapting the system to a new domain requires a high amount of manual labour, such as specifying and implement- ing relation–specific extraction patterns manually (cf. Fig. 1) or annotating large amounts of training cor- pora (cf. Fig. 2). These adaptations have to be made offline, i.e., before the specific IE system is actually made. Consequently, current IE technology is highly statical and inflexible with respect to a timely adapta- tion to new requirements in the form of new topics. Figure 1: A hand-coded rule–based IE–system (schemat- ically): A topic expert implements manually task–specific extraction rules on the basis of her manual analysis of a representative corpus. 1.1. Our goal The goal of our IE research is the conception and im- plementation of core IE technology to produce a new Figure 2: A data–oriented IE system (schematically): The task–specific extraction rules are automatically acquired by means of Machine Learning algorithms, which are using a sufficiently large enough corpus of topic–relevant docu- ments. These documents have to be collected and costly annotated by a topic–expert. IE system automatically for a given topic. Here, the pre–knowledge about the information request is given by a user online to the IE core system (called IDEX) in the form of a topic description (cf. Fig. 3). This initial information source is used to retrieve relevant documents and extract and cluster relations in an un- supervised way. In this way, IDEX is able to adapt much better to the dynamic information space, in par- ticular because no predefined patterns of relevant re- lations have to be specified, but relevant patterns are determined online. Our system consists of a front-end, which provides the user with a GUI for interactively in- specting information extracted from topic-related web documents, and a back-end, which contains the rela- tion extraction and …

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