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We present a novel parser combination scheme that works by reparsing input sentences once they have already been parsed by several different parsers. We apply this idea to dependency and constituent parsing, generating results that surpass state-of-the-art accuracy levels for individual parsers.
We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabil-istic generalized LR dependency parsing. Parser actions are determined by a classifi-er, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared(More)
Recent research has shown that a balanced harmonic mean (F1 measure) of unigram precision and recall outperforms the widely used BLEU and NIST metrics for Machine Translation evaluation in terms of correlation with human judgments of translation quality. We show that significantly better correlations can be achieved by placing more weight on recall than on(More)
 Most data-driven dependency parsing approaches assume that sentence structure is represented as trees. Although trees have several desirable properties from both computational and linguistic perspectives, the structure of linguistic phenomena that goes beyond shallow syntax often cannot be fully captured by tree representations. We present a parsing(More)
We investigate novel approaches to responsive overlap behaviors in dialogue systems, opening possibilities for systems to interrupt, acknowledge or complete a user's utterance while it is still in progress. Our specific contributions are a method for determining when a system has reached a point of maximal understanding of an ongoing user utterance, and a(More)
MOTIVATION While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides(More)
Background: Extracting Protein-Protein Interactions (PPI) from research papers is a way of translating information from English to the language used by the databases that store this information. With recent advances in automatic PPI detection, it is now possible to speed up this process considerably. Syntactic features from different parsers for biomedical(More)