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We present the results of the Joint Student Response Analysis and 8th Recognizing Tex-tual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual entailment. The task of giving feedback on student answers requires semantic inference and therefore is related to recognizing textual entailment. Thus, we offered to(More)
We describe an application of sentence alignment techniques and approximate string matching to the problem of extracting lexicographically interesting word-word pairs from multilingual corpora. Since our interest is in support systems for lexicographers rather than in fully automatic construction of lexicons, we would like to provide access to parameters(More)
This paper describes a novel approach to syntactically-informed evaluation of machine translation (MT). Using a statistical, treebank-trained parser, we extract word-word dependencies from reference translations and then compile these dependencies into a representation that allows candidate translations to be evaluated by string comparisons, as is done in(More)
In this paper, we describe a resource-light system for the automatic morphological analysis and tagging of Russian. We eschew the use of extensive resources (particularly, large annotated corpora and lexicons), exploiting instead (i) pre-existing annotated corpora of Czech; (ii) an unannotated corpus of Russian. We show that our approach has benefits, and(More)
We describe an implementation in Carpenter's typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al. We examine their method for resolving parallelism-dependent anaphora and show that there is a coherent feature-structural rendition of this type of grammar which uses the operations of prwrity union and(More)
A Distributional Model 2 Abstract One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence probability distributions, and empirically validate the model through(More)