Robert J. Gaizauskas

Learn More
In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlike most previous work on event annotation, TimeML captures three distinct phenomena in temporal markup: (1) it systematically anchors(More)
Much progress has been made in the provision of reusable data resources for Natural Language Engineering, such as grammars, lexicons, thesauruses. Although a number of projects have addressed the provision of reusable algorithmic resources (or 'tools'), takeup of these resources has been relatively slow. This paper describes GATE, a General Architecture for(More)
The TempEval task proposes a simple way to evaluate automatic extraction of temporal relations. It avoids the pitfalls of evaluating a graph of inter-related labels by defining three sub tasks that allow pairwise evaluation of temporal relations. The task not only allows straightforward evaluation, it also avoids the complexities of full temporal parsing.
Graphical CASE (Computer Aided Software Engineering) tools provide considerable help in documenting the output of the Analysis and Design stages of software development and can assist in detecting incompleteness and inconsistency in an analysis. However, these tools do not contribute to the initial, difficult stage of the analysis process, that of(More)
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 part of a larger research e ort into information extraction underway in our group. LaSIE-II was used to carry out all ve of the MUC-7 tasks and was, in fact,(More)
TempEval is a framework for evaluating systems that automatically annotate texts with temporal relations. It was created in the context of the SemEval 2007 workshop and uses the TimeML annotation language. The evaluation consists of three subtasks of temporal annotation: anchoring an event to a time expression in the same sentence, anchoring an event to the(More)
Temporal expressions are words or phrases that describe a point, duration or recurrence in time. Automatically annotating these expressions is a research goal of increasing interest. Recognising them can be achieved with supervised machine learning, but interpreting them accurately (normalisation) is a complex task requiring human knowledge. In this paper,(More)
We describe the Sheffield system used in TempEval-2007. Our system takes a machine-learning (ML) based approach, treating temporal relation assignment as a simple classification task and using features easily derived from the TempEval data, i.e. which do not require ‘deeper’ NLP analysis. We aimed to explore three questions: (1) How well would a ‘lite’(More)