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Domain similarity for parsing

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  • It is well known that parsing accuracy suffers when a model is applied to out-of-domain data. It is also known that the most beneficial data to parse a given domain is data that matches the domain (Sekine, 1997; Gildea, 2001). Hence, an important task is to select appropriate domains. However, most previous work on domain adaptation relied on the implicit assumption that domains are somehow given.

    pdf11p hongdo_1 12-04-2013 35 2   Download

  • Hungarian is a stereotype of morphologically rich and non-configurational languages. Here, we introduce results on dependency parsing of Hungarian that employ a 80K, multi-domain, fully manually annotated corpus, the Szeged Dependency Treebank. We show that the results achieved by state-of-the-art data-driven parsers on Hungarian and English (which is at the other end of the configurational-nonconfigurational spectrum) are quite similar to each other in terms of attachment scores.

    pdf11p bunthai_1 06-05-2013 36 3   Download

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