Timur Gilmanov

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We describe the IUCL+ system for the shared task of the First Workshop on Computational Approaches to Code Switching (Solorio et al., 2014), in which participants were challenged to label each word in Twitter texts as a named entity or one of two candidate languages. Our system combines character n-gram probabilities, lexical probabilities, word label(More)
It is well known that word aligned parallel corpora are valuable linguistic resources. Since many factors affect automatic alignment quality, manual post-editing may be required in some applications. While there are several state-of-the-art word-aligners, such as GIZA++ and Berkeley, there is no simple visual tool that would enable correcting and editing(More)
We investigate whether nonconfigurational languages, which display more word order variation than configurational ones, require more training data for a phenomenon to be parsed successfully. We perform a tightly controlled study comparing the dative alternation for English (a configurational language), German, and Russian (both non-configurational). More(More)
It is well known that parallel corpora are valuable linguistic resources. One of the benefits of such corpora is that they allow for the building an annotated corpus for resource-poor languages via crosslanguage transfer. That is, given accurate alignment between a word from a source language and its equivalent in a target language, some linguistic(More)
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