Iddo Greental

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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)
We present a new framework for textual en-tailment, which provides a modular integration between knowledge-based exact inference and cost-based approximate matching. Diverse types of knowledge are uniformly represented as entailment rules, which were acquired both manually and automatically. Our proof system operates directly on parse trees, and infers new(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)
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(More)
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