Titlebook: Machine Learning Challenges; Evaluating Predictiv Joaquin Qui?onero-Candela,Ido Dagan,Florence d’Alc Conference proceedings 2006 Springer-V

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書(shū)目名稱(chēng)Machine Learning Challenges影響因子(影響力)




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The 2005 PASCAL Visual Object Classes Challenge, object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

STALL

What Syntax Can Contribute in the Entailment Task,tegorization can be accurately predicted based solely on syntactic cues. Two human annotators examined each pair, showing that a surprisingly large proportion of the data – 34% of the test items – can be handled with syntax alone, while adding information from a general-purpose thesaurus increases this to 48%.

Derogate

A Lexical Alignment Model for Probabilistic Textual Entailment,ic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the . challenge dataset along with some analysis.

Wernickes-area

Partial Predicate Argument Structure Matching for Entailment Determination,cture matching combined with a WordNet-based lexical similarity measure. In this paper we describe experiments with different system settings conducted to assess the potential and limitations of partial predicate-argument structures in textual entailment determination.

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