6,610 research outputs found
The CoNLL 2007 shared task on dependency parsing
The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Parsing as Reduction
We reduce phrase-representation parsing to dependency parsing. Our reduction
is grounded on a new intermediate representation, "head-ordered dependency
trees", shown to be isomorphic to constituent trees. By encoding order
information in the dependency labels, we show that any off-the-shelf, trainable
dependency parser can be used to produce constituents. When this parser is
non-projective, we can perform discontinuous parsing in a very natural manner.
Despite the simplicity of our approach, experiments show that the resulting
parsers are on par with strong baselines, such as the Berkeley parser for
English and the best single system in the SPMRL-2014 shared task. Results are
particularly striking for discontinuous parsing of German, where we surpass the
current state of the art by a wide margin
Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly
Recent years have seen considerable success in the generation of automatically obtained wide-coverage deep grammars for natural language processing, given reliable
and large CFG-like treebanks. For research within Lexical Functional Grammar framework, these deep grammars are
typically based on an extended PCFG parsing scheme from which dependencies are extracted. However, increasing success in statistical dependency parsing suggests that such deep grammar approaches to statistical parsing could be streamlined. We explore this novel approach to deep
grammar parsing within the framework of LFG in this paper, for French, showing that best results (an f-score of 69.46) for the established integrated architecture may be obtained for French
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