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This paper shows how finite approximations of long distance dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II(More)
Data-driven approaches to machine translation (MT) achieve state-of-the-art results. Many syntax-aware approaches, such as ExampleBased MT and Data-Oriented Translation, make use of tree pairs aligned at sub-sentential level. Obtaining sub-sentential alignments manually is time-consuming and error-prone, and requires expert knowledge of both source and(More)
We describe DCU’s LFG dependencybased metric submitted to the shared evaluation task of WMT-MetricsMATR 2010. The metric is built on the LFG F-structurebased approach presented in (Owczarzak et al., 2007). We explore the following improvements on the original metric: 1) we replace the in-house LFG parser with an open source dependency parser that directly(More)
The development of large coverage, rich unification(constraint-) based grammar resources is very time consuming, expensive and requires lots of linguistic expertise. In this paper we report initial results on a new methodology that attempts to partially automate the development of substantial parts of large coverage, rich unification(constraint-) based(More)
A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a(More)
The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. The ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. In this paper we introduce a novel platform for fast(More)
We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings. We constrain the translation of an input sentence using the most similar(More)