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- 123 type InProceedings.
- 123 label "Evaluating Summaries Automatically - A system Proposal".
- 123 sameAs 123.
- 123 abstract "We propose in this paper an automatic evaluation procedure based on a metric which could provide summary evaluation without human assistance. Our system includes two metrics, which are presented and discussed. The first metric is based on a known and powerful statistical test, the X2 goodness-of-fit test, and has been used in several applications. The second metric is derived from three common metrics used to evaluate Natural Language Processing (NLP) systems, namely precision, recall and f-measure. The combination of these two metrics is intended to allow one to assess the quality of summaries quickly, cheaply and without the need of human intervention, minimizing though, the role of subjective judgment and bias.".
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- 123 keyword "Evaluation methodologies".
- 123 keyword "Summarisation".
- 123 keyword "Tools, systems, applications".
- 123 title "Evaluating Summaries Automatically - A system Proposal".
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- 124 creator pierre-nugues.
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- 124 label "Comparing Dependency and Constituent Syntax for Frame-semantic Analysis".
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- 124 abstract "We address the question of which syntactic representation is best suited for role-semantic analysis of English in the FrameNet paradigm. We compare systems based on dependencies and constituents, and a dependency syntax with a rich set of grammatical functions with one with a smaller set. Our experiments show that dependency-based and constituent-based analyzers give roughly equivalent performance, and that a richer set of functions has a positive influence on argument classification for verbs.".
- 124 hasAuthorList authorList.
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- 126 label "Sentence Alignment in DPC: Maximizing Precision, Minimizing Human Effort".
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- 126 abstract "A wide spectrum of multilingual applications have aligned parallel corpora as their prerequisite. The aim of the project described in this paper is to build a multilingual corpus where all sentences are aligned at very high precision with a minimal human effort involved. The experiments on a combination of sentence aligners with different underlying algorithms described in this paper showed that by verifying only those links which were not recognized by at least two aligners, an error rate can be reduced by 93.76% as compared to the performance of the best aligner. Such manual involvement concerned only a small portion of all data (6%). This significantly reduces a load of manual work necessary to achieve nearly 100% accuracy of alignment.".
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- 126 title "Sentence Alignment in DPC: Maximizing Precision, Minimizing Human Effort".
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