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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)
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)
Requiring only category names as user input is a highly attractive, yet hardly explored, setting for text categorization. Earlier bootstrap-ping results relied on similarity in LSA space, which captures rather coarse contextual similarity. We suggest improving this scheme by identifying concrete references to the category name's meaning, obtaining a special(More)