The automatic component of the LINGSTAT machine-aided translation system

We present the newest implementation of the LINGSTAT machine-aided translation system. The moat signiflcsat change from earlier versions is a new set of modules that pro-duce a draft translation of the document for the user to refer to or modify. This paper describes these modules, with spe-cial emphasis on an automatically trained lexicalized gram-mar used in the parsing module. Some preHminary results from the January 1994 ARPA evaluation are reported. 1

Topic Detection and Tracking Pilot Study Final Report

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study

Correction: Bryophyte-Cyanobacteria Associations during Primary Succession in Recently Deglaciated Areas of Tierra del Fuego (Chile)

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of “topic” than was used in the pilot study.  The following individuals participated in the research reported.  James Allan, UMass  Brian Archibald, CMU  Doug Beeferman, CMU  Adam Berger, CMU  Ralf Brown, CMU  Jaime Carbonell, CMU  Ira Carp, Dragon  Bruce Croft, UMass,  George Doddington, DARPA  Larry Gillick, Dragon  Alex Hauptmann, CMU  John Lafferty, CMU  Victor Lavrenko, UMass  Xin Liu, CMU  Steve Lowe, Dragon  Paul van Mulbregt, Dragon  Ron Papka, UMass  Thomas Pierce, CMU  Jay Ponte, UMass  Mike Scudder, UMass  Charles Wayne, DARPA  Jon Yamron, Dragon  Yiming Yang, CMU</p

Topic Detection and Tracking Pilot Study

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of &quot;topic&quot; than was used in the pilot study. The following individuals participat..

LINGSTAT: AN INTERACTIVE, MACHINE-AIDED TRANSLATION SYSTEM*

In this paper we present the first implementation of LING-STAT, an interactive machine translation system designed to increase the productivity of a user, with little knowledge of the source language, in translating or extracting information from foreign language documents. In its final form, LING-STAT will make use of statistical information gathered from parallel and single-language corpora, and linguistic information at all levels (lexical, syntactic, and semantic). 1