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In addition to a high accuracy, short parsing and training times are the most important properties of a parser. However, parsing and training times are still relatively long. To determine why, we analyzed the time usage of a dependency parser. We illustrate that the mapping of the features onto their weights in the support vector machine is the major factor(More)
Most current dependency parsers presuppose that input words have been morphologically disambiguated using a part-of-speech tagger before parsing begins. We present a transitionbased system for joint part-of-speech tagging and labeled dependency parsing with nonprojective trees. Experimental evaluation on Chinese, Czech, English and German shows consistent(More)
This demonstration presents a highperformance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the tokenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system’s two main components draw on improved versions of a state-of-the-art dependency(More)
Most of the known stochastic sentence generators use syntactically annotated corpora, performing the projection to the surface in one stage. However, in full-fledged text generation, sentence realization usually starts from semantic (predicate-argument) structures. To be able to deal with semantic structures, stochastic generators require semantically(More)
We describe improvements to our 2008 system that result in a top-performing summarization system. The motivating ideas are (1) improve sentence boundary detection to avoid damaging errors in preprocessing; (2) prune sentences that are unlikely to work well in a summary; (3) leverage sentence position to improve update summarization; (4) focus on(More)
Transition-based dependency parsers are often forced to make attachment decisions at a point when only partial information about the relevant graph configuration is available. In this paper, we describe a model that takes into account complete structures as they become available to rescore the elements of a beam, combining the advantages of transition-based(More)
Joint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages. Starting from a transition-based model for joint part-of-speech tagging and dependency parsing, we explore different ways of integrating morphological features into the model. We also investigate the use of rule-based(More)
We present a novel, count-based approach to obtaining inter-lingual word representations based on inverted indexing of Wikipedia. We present experiments applying these representations to 17 datasets in document classification, POS tagging, dependency parsing, and word alignment. Our approach has the advantage that it is simple, computationally efficient and(More)
The algorithm IS-FP takes up the idea from the IS-FBN algorithm developed for the shared task 2007. Both algorithms learn the individual attribute selection style for each human that provided referring expressions to the corpus. The IS-FP algorithm was developed with two additional goals (1) to improve the indentification time that was poor for the FBN(More)