2,895 research outputs found
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
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
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Towards a machine-learning architecture for lexical functional grammar parsing
Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also
recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able
to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously
lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages
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