Nathan Green

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In this paper we describe a specialized keyboard for text entry that maps four rows of a standard keyboard onto the home row, with different characters encoded via modifier keys and multi-tap input. Use of the keyboard also relies on lexicon-based disambiguation. This design has two motivations: limiting physical space requirements and capitalizing on user(More)
Accuracy of dependency parsers is one of the key factors limiting the quality of dependency-based machine translation. This paper deals with the influence of various dependency parsing approaches (and also different training data size) on the overall performance of an English-to-Czech dependency-based statistical translation system implemented in the Treex(More)
Dependency parsing has made many advancements in recent years, in particular for English. There are a few dependency parsers that achieve comparable accuracy scores with each other but with very different types of errors. This paper examines creating a new dependency structure through ensemble learning using a hybrid of the outputs of various parsers. We(More)
We introduce and describe ongoing work in our Indonesian dependency treebank. We described characteristics of the source data as well as describe our annotation guidelines for creating the dependency structures. Reported within are the results from the start of the Indonesian dependency treebank. We also show ensemble dependency parsing and self training(More)
Dependency parsing has been shown to improve NLP systems in certain languages and in many cases helps achieve state of the art results in NLP applications, in particular applications for free word order languages. Morphologically rich languages are often short on training data or require much higher amounts of training data due to the increased size of(More)
Flat noun phrase structure was, up until recently , the standard in annotation for the Penn Treebanks. With the recent addition of internal noun phrase annotation, dependency parsing and applications down the NLP pipeline are likely affected. Some machine translation systems, such as TectoMT, use deep syntax as a language transfer layer. It is proposed that(More)
Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. Traditional co-clustering techniques are inapplicable to problems where the relationship between the instances (rows) and features (columns) evolve over time. Not only is it important for the clustering(More)
Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. The structures may have more complexity due to their coordination structure or attachment rules. As dependency parses are basic structures in which other systems are built upon, it would seem(More)
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