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Search Results: 1 - 10 of 318754 matches for " Robert J. Gaizauskas "
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New Methods, Current Trends and Software Infrastructure for NLP
Hamish Cunningham,Yorick Wilks,Robert J. Gaizauskas
Computer Science , 1996,
Abstract: The increasing use of `new methods' in NLP, which the NeMLaP conference series exemplifies, occurs in the context of a wider shift in the nature and concerns of the discipline. This paper begins with a short review of this context and significant trends in the field. The review motivates and leads to a set of requirements for support software of general utility for NLP research and development workers. A freely-available system designed to meet these requirements is described (called GATE - a General Architecture for Text Engineering). Information Extraction (IE), in the sense defined by the Message Understanding Conferences (ARPA \cite{Arp95}), is an NLP application in which many of the new methods have found a home (Hobbs \cite{Hob93}; Jacobs ed. \cite{Jac92}). An IE system based on GATE is also available for research purposes, and this is described. Lastly we review related work.
A General Architecture for Language Engineering (GATE) - a new approach to Language Engineering R&D
Hamish Cunningham,Robert J. Gaizauskas,Yorick Wilks
Computer Science , 1996,
Abstract: This report argues for the provision of a common software infrastructure for NLP systems. Current trends in Language Engineering research are reviewed as motivation for this infrastructure, and relevant recent work discussed. A freely-available system called GATE is described which builds on this work.
An Annotation Scheme for Reichenbach's Verbal Tense Structure
Leon Derczynski,Robert Gaizauskas
Computer Science , 2012,
Abstract: In this paper we present RTMML, a markup language for the tenses of verbs and temporal relations between verbs. There is a richness to tense in language that is not fully captured by existing temporal annotation schemata. Following Reichenbach we present an analysis of tense in terms of abstract time points, with the aim of supporting automated processing of tense and temporal relations in language. This allows for precise reasoning about tense in documents, and the deduction of temporal relations between the times and verbal events in a discourse. We define the syntax of RTMML, and demonstrate the markup in a range of situations.
USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2
Leon Derczynski,Robert Gaizauskas
Computer Science , 2012,
Abstract: We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and anchors temporal expressions, and a maximum entropy classifier assigns temporal link labels, based on features that include descriptions of associated temporal signal words. USFD2 identified temporal expressions successfully, and correctly classified their type in 90% of cases. Determining the relation between an event and time expression in the same sentence was performed at 63% accuracy, the second highest score in this part of the challenge.
Using Signals to Improve Automatic Classification of Temporal Relations
Leon Derczynski,Robert Gaizauskas
Computer Science , 2012,
Abstract: Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.
Analysing Temporally Annotated Corpora with CAVaT
Leon Derczynski,Robert Gaizauskas
Computer Science , 2012,
Abstract: We present CAVaT, a tool that performs Corpus Analysis and Validation for TimeML. CAVaT is an open source, modular checking utility for statistical analysis of features specific to temporally-annotated natural language corpora. It provides reporting, highlights salient links between a variety of general and time-specific linguistic features, and also validates a temporal annotation to ensure that it is logically consistent and sufficiently annotated. Uniquely, CAVaT provides analysis specific to TimeML-annotated temporal information. TimeML is a standard for annotating temporal information in natural language text. In this paper, we present the reporting part of CAVaT, and then its error-checking ability, including the workings of several novel TimeML document verification methods. This is followed by the execution of some example tasks using the tool to show relations between times, events, signals and links. We also demonstrate inconsistencies in a TimeML corpus (TimeBank) that have been detected with CAVaT.
A Corpus-based Study of Temporal Signals
Leon Derczynski,Robert Gaizauskas
Computer Science , 2012,
Abstract: Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are conveyed in text via va rious linguistic mechanisms including the use of expressions such as "before", "after" or "during" that explicitly assert a temporal relation -- temporal signals. In this paper, we investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expres sions in the TimeBank annotated corpus.
Evaluating a Focus-Based Approach to Anaphora Resolution
Saliha Azzam,Kevin Humphreys,Robert Gaizauskas
Computer Science , 1998,
Abstract: We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understanding Conference) Information Extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. Extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. Results are compared with the results of a simpler heuristic-based approach.
Report of the Study Group on Assessment and Evaluation
Richard Crouch,Robert Gaizauskas,Klaus Netter
Computer Science , 1996,
Abstract: This is an interim report discussing possible guidelines for the assessment and evaluation of projects developing speech and language systems. It was prepared at the request of the European Commission DG XIII by an ad hoc study group, and is now being made available in the form in which it was submitted to the Commission. However, the report is not an official European Commission document, and does not reflect European Commission policy, official or otherwise. After a discussion of terminology, the report focusses on combining user-centred and technology-centred assessment, and on how meaningful comparisons can be made of a variety of systems performing different tasks for different domains. The report outlines the kind of infra-structure that might be required to support comparative assessment and evaluation of heterogenous projects, and also the results of a questionnaire concerning different approaches to evaluation.
Compacting the Penn Treebank Grammar
Alexander Krotov,Mark Hepple,Robert Gaizauskas,Yorick Wilks
Computer Science , 1999,
Abstract: Treebanks, such as the Penn Treebank (PTB), offer a simple approach to obtaining a broad coverage grammar: one can simply read the grammar off the parse trees in the treebank. While such a grammar is easy to obtain, a square-root rate of growth of the rule set with corpus size suggests that the derived grammar is far from complete and that much more treebanked text would be required to obtain a complete grammar, if one exists at some limit. However, we offer an alternative explanation in terms of the underspecification of structures within the treebank. This hypothesis is explored by applying an algorithm to compact the derived grammar by eliminating redundant rules -- rules whose right hand sides can be parsed by other rules. The size of the resulting compacted grammar, which is significantly less than that of the full treebank grammar, is shown to approach a limit. However, such a compacted grammar does not yield very good performance figures. A version of the compaction algorithm taking rule probabilities into account is proposed, which is argued to be more linguistically motivated. Combined with simple thresholding, this method can be used to give a 58% reduction in grammar size without significant change in parsing performance, and can produce a 69% reduction with some gain in recall, but a loss in precision.
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