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This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar(More)
The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools and techniques of MT alignment do not readily transfer to NLI, where one cannot assume semantic equivalence , and for which large volumes of bitext are(More)
We propose an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features , without full semantic interpretation. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and(More)
This paper advocates a new architecture for tex-tual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally(More)
This paper proposes a new architecture for tex-tual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally(More)
We describe an approach to textual inference that improves alignments at both the typed dependency level and at a deeper semantic level. We present a machine learning approach to alignment scoring, a stochas-tic search procedure, and a new tool that finds deeper semantic alignments, allowing rapid development of semantic features over the aligned graphs.(More)
We present a machine learning approach to robust textual inference, in which parses of the text and the hypothesis sentences are used to measure their asymmetric " similarity " , and thereby to decide if the hypothesis can be inferred. This idea is realized in two different ways. In the first, each sentence is represented as a graph (extracted from a(More)