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Lexical-semantic resources are used extensively for applied semantic inference, yet a clear quantitative picture of their current utility and limitations is largely missing. We propose system-and application-independent evaluation and analysis methodologies for resources' performance , and systematically apply them to seven prominent resources. Our findings(More)
Semantic inference is an important component in many natural language understanding applications. Classical approaches to semantic inference rely on complex logical representations. However, practical applications usually adopt shallower lexical or lexical-syntactic representations , but lack a principled inference framework. We propose a generic semantic(More)
This paper describes Bar-Ilan University's submissions to RTE-5. This year we fo-cused on the Search pilot, enhancing our entailment system to address two main issues introduced by this new setting: scal-ability and, primarily, document-level discourse. Our system achieved the highest score on the Search task amongst participating groups, and proposes first(More)
Semantic inference is often modeled as application of entailment rules, which specify generation of entailed sentences from a source sentence. Efficient generation and representation of entailed consequents is a fundamental problem common to such inference methods. We present a new data structure, termed compact forest, which allows efficient generation and(More)
Recognizing entailment at the lexical level is an important and commonly-addressed component in textual inference. Yet, this task has been mostly approached by simplified heuris-tic methods. This paper proposes an initial probabilistic modeling framework for lexical entailment, with suitable EM-based parameter estimation. Our model considers prominent(More)
Texts are commonly interpreted based on the entire discourse in which they are situated. Discourse processing has been shown useful for inference-based application ; yet, most systems for textual entail-ment – a generic paradigm for applied inference – have only addressed discourse considerations via off-the-shelf corefer-ence resolvers. In this paper we(More)