The paper presents an automatic acquisition of linguistic patterns that can be used for knowledge based information extraction from texts. In knowledge based information extraction, linguistic patterns play a central role in the recognition and classification of input texts. Although the knowledge based approach has been proved effective for information extraction on limited domains, there are difficulties in construction of a large number of domain specific linguistic patterns. Manual creation of patterns is time consuming and error prone, even for a small application domain. To solve the scalability and the portability problem, an automatic acquisition of patterns must be provided. We present the PALKA (Parallel Automatic Linguistic Knowledge Acquisition) system that acquires linguistic patterns from a set of domain specific training texts and their desired outputs. A specialized representation of patterns called FP structures has been defined. Patterns are constructed in the form of FP structures from training texts, and the acquired patterns are tuned further through the generalization of semantic constraints. Inductive learning mechanism is applied in the generalization step. The PALKA system has been used to generate patterns for our information extraction system developed for the fourth Message Understanding Conference (MUC-4).>

Article Free Access Share on PALKA: a system for lexical knowledge acquisition Authors: Jun-Tae Kim Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CAView Profile , Dan I. Moldovan Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CAView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 124–131https://doi.org/10.1145/170088.170116Published:01 December 1993Publication History 3citation551DownloadsMetricsTotal Citations3Total Downloads551Last 12 Months8Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Frame-based systems or semantic networks have been generally used for knowledge representation. In such a knowledge representation system, concepts in the knowledge base are organized based on the subsumption relation between concepts, and classification is a process of constructing a concept hierarchy according to the subsumption relationships. Since the classification process involves search and subsumption test between concepts, classification on a large knowledge base may become unacceptably slow, especially for real-time applications. In this paper, a massively parallel classification and property retrieval algorithm on a marker passing architecture is presented. The subsumption relation is first defined by using the set relationship, and the parallel classification algorithm is described based on that relationship. In this algorithm, subsumption test between two concepts is done by parallel marker passing and multiple subsumption tests are performed simultaneously. To investigate the performance of the algorithm, time complexities of sequential and parallel classification are compared. Simulation of the parallel classification algorithm was performed using the SNAP (Semantic Network Array Processor) simulator, and the influence of several factors on the execution time is discussed.>