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University of Sheffield: Description of the LaSIE-II System as Used for MUC-7
The University of She eld NLP group took part in MUC-7 using the LaSIE-II system, an evolution of the LaSIE (Large Scale Information Extraction) system rst created for participation in MUC-6 [9] and
Two applications of information extraction to biological science journal articles: enzyme interactions and protein structures.
TLDR
This paper describes how an information extraction system designed to participate in the MUC exercises has been modified for two bioinformatics applications: EMPathIE, concerns with enzyme and metabolic pathways; and PASTA, concerned with protein structure.
University of Sheffield: description of the LaSIE system as used for MUC-6
The LaSIE (Large Scale Information Extraction) system has been developed at the University of Sheffield as part of an ongoing research effort into information extraction and, more generally, natural
Event coreference for information extraction
We propose a general approach for performing event coreference and for constructing complex event representations, such as those required for information extraction tasks. Our approach is based on a
GATE - a TIPSTER-based General Architecture for Text Engineering
TLDR
This paper classifies and reviews current approaches to software infrastructure for research, development and delivery of NLP systems, and describes GATE, a General Architecture for Text Engineering.
Term Recognition and Classification in Biological Science Journal Articles
TLDR
The apparatus includes an arm pivoted between its end portions, a foot pedal for operating one of the end portions of the arm, and a switch operated by the other end portion of the hand, which is part of the invention.
University of Sheffield TREC-8 Q&A System
TLDR
This paper concentrates on describing the modifications made to the existing information extraction system to allow it to participate in the Q & A task.
GATE: an environment to support research and development in natural language engineering
TLDR
GATE (General Architecture for Text Engineering)-aims to advance research in the area of machine processing of natural languages by providing a software infrastructure on top of which heterogeneous NL component modules may be evaluated and refined individually or may be combined into larger application systems.
Quantitative evaluation of coreference algorithms in an information extraction system
TLDR
The MUC-6 coreference task and the approach taken by the Large Scale Information Extraction (LaSIE) system developed at the University of Sheeeld are described, demonstrating both the utility of quantative analysis for assessing coreference algorithms and the exibility of the approach to coreference which provides a framework that facilitates experimentation with alternative techniques.
A combined IR/NLP approach to question answering against large text collections
We describe an approach to finding literal answer strings to natural language questions in large text collections. The approach involves linking an IR system with an NLP system that performs
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