Nancy Chinchor

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For many text processing systems, such identifiers are recognized primarily using local pattern-matching techniques. The TEI (Text Encoding Initiative) Guidelines for Electronic Text Encoding and Interchange cover such identifiers (plus abbreviations) together in section 6.4 and explain that the identifiers comprise "textual features which it is often(More)
The task of Coreference (CO) had its origins in Semeval, an attempt after MUC-5 to define semantic research tasks that needed to be solved to be successful at generating scenario templates. In the MUC evaluations, only coreference of type identity was marked and scored [3]. The following example from MUC-7 (New York Times News Service) illustrates identity(More)
The MUC-4 evaluation metrics measure the performance of the message understanding systems . This paper describes the scoring algorithms used to arrive at the metrics as well as the improvements that were made to th e MUC-3 methods . MUC-4 evaluation metrics were stricter than those used in MUC-3. Given the differences in scoring between MUC-3 and MUC-4, the(More)
This paper describes and analyzes the results of the Third Message Understanding Conference (MUC-3). It reviews the purpose, history, and methodology of the conference, summarizes the participating systems, discusses issues of measuring system effectiveness, describes the linguistic phenomena tests, and provides a critical look at the evaluation in terms of(More)
The MUC-5 Scoring System is evaluation software that aligns and scores the templates produced by th e information extraction systems under evaluation in comparison to an "answer key" created by humans . The Scoring System produces comprehensive summary reports showing the overall scores for the templates in the test set ; these may be supplemented by(More)
Information extraction in the sense of the Message Understanding Conferences has been traditionally defined as the extraction of information from a text in the form of text strings and processed text strings which are placed into slots labeled to indicate the kind of information that can fill them. So, for example, a slot labeled NAME would contain a name(More)
Recent advances in text analysis have led to finer-grained semantic analysis, including automatic sentiment analysis— the task of measuring documents, or chunks of text, based on emotive categories, such as positive or negative. However, considerably less progress has been made on efficient ways of exploring these measurements. This paper discusses(More)
In November, 1996, the Message Understanding Conference-6 (MUC-6) evaluation of named entity identification demonstrated that systems are approaching human performance on English language texts [10]. Informal and anonymous, the MET provided a new opportunity to assess progress on the same task in Spanish, Japanese, and Chinese. Preliminary results indicate(More)