780 research outputs found
Nightmare at test time: How punctuation prevents parsers from generalizing
Punctuation is a strong indicator of syntactic structure, and parsers trained
on text with punctuation often rely heavily on this signal. Punctuation is a
diversion, however, since human language processing does not rely on
punctuation to the same extent, and in informal texts, we therefore often leave
out punctuation. We also use punctuation ungrammatically for emphatic or
creative purposes, or simply by mistake. We show that (a) dependency parsers
are sensitive to both absence of punctuation and to alternative uses; (b)
neural parsers tend to be more sensitive than vintage parsers; (c) training
neural parsers without punctuation outperforms all out-of-the-box parsers
across all scenarios where punctuation departs from standard punctuation. Our
main experiments are on synthetically corrupted data to study the effect of
punctuation in isolation and avoid potential confounds, but we also show
effects on out-of-domain data.Comment: Analyzing and interpreting neural networks for NLP, EMNLP 2018
worksho
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
The incremental use of morphological information and lexicalization in data-driven dependency parsing
Typological diversity among the natural languages of the world poses interesting challenges for the models and algorithms used in syntactic parsing. In this paper, we apply a data-driven dependency parser to Turkish, a language characterized by rich morphology and flexible constituent order, and study the effect of employing varying amounts of morpholexical information on parsing performance. The investigations show that accuracy can be improved by using representations based on inflectional groups rather than word forms, confirming earlier studies. In addition, lexicalization and the use of rich morphological features are found to have a positive effect. By combining all these techniques, we obtain the highest reported accuracy for parsing the Turkish Treebank
Crossings as a side effect of dependency lengths
The syntactic structure of sentences exhibits a striking regularity:
dependencies tend to not cross when drawn above the sentence. We investigate
two competing explanations. The traditional hypothesis is that this trend
arises from an independent principle of syntax that reduces crossings
practically to zero. An alternative to this view is the hypothesis that
crossings are a side effect of dependency lengths, i.e. sentences with shorter
dependency lengths should tend to have fewer crossings. We are able to reject
the traditional view in the majority of languages considered. The alternative
hypothesis can lead to a more parsimonious theory of language.Comment: the discussion section has been expanded significantly; in press in
Complexity (Wiley
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 of Turkish
The suitability of different parsing methods for different languages is an important topic in
syntactic parsing. Especially lesser-studied languages, typologically different from the languages
for which methods have originally been developed, poses interesting challenges in this respect.
This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative
free constituent order language that can be seen as the representative of a wider class
of languages of similar type. Our investigations show that morphological structure plays an
essential role in finding syntactic relations in such a language. In particular, we show that
employing sublexical representations called inflectional groups, rather than word forms, as the
basic parsing units improves parsing accuracy. We compare two different parsing methods, one
based on a probabilistic model with beam search, the other based on discriminative classifiers and
a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless
of parsing method.We examine the impact of morphological and lexical information in detail and
show that, properly used, this kind of information can improve parsing accuracy substantially.
Applying the techniques presented in this article, we achieve the highest reported accuracy for
parsing the Turkish Treebank
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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