Eyal Shnarch

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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)
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 systemand application-independent evaluation and analysis methodologies for resources’ performance, and systematically apply them to seven prominent resources. Our findings(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)
Requiring only category names as user input is a highly attractive, yet hardly explored, setting for text categorization. Earlier bootstrapping 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)
This paper describes Bar-Ilan University’s submissions to RTE-5. This year we focused on the Search pilot, enhancing our entailment system to address two main issues introduced by this new setting: scalability and, primarily, document-level discourse. Our system achieved the highest score on the Search task amongst participating groups, and proposes first(More)
Identifying textual inferences, where the meaning of one text follows from another, is a general underlying task within many natural language applications. Commonly, it is approached either by generative syntactic-based methods or by “lightweight” heuristic lexical models. We suggest a model which is confined to simple lexical information, but is formulated(More)
Semantic inference is an important component in many natural language understanding applications. Classical approaches to semantic inference rely on logical representations for meaning, which may be viewed as being “external” to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations,(More)