2,614 research outputs found
A Processing Model for Free Word Order Languages
Like many verb-final languages, Germn displays considerable word-order
freedom: there is no syntactic constraint on the ordering of the nominal
arguments of a verb, as long as the verb remains in final position. This effect
is referred to as ``scrambling'', and is interpreted in transformational
frameworks as leftward movement of the arguments. Furthermore, arguments from
an embedded clause may move out of their clause; this effect is referred to as
``long-distance scrambling''. While scrambling has recently received
considerable attention in the syntactic literature, the status of long-distance
scrambling has only rarely been addressed. The reason for this is the
problematic status of the data: not only is long-distance scrambling highly
dependent on pragmatic context, it also is strongly subject to degradation due
to processing constraints. As in the case of center-embedding, it is not
immediately clear whether to assume that observed unacceptability of highly
complex sentences is due to grammatical restrictions, or whether we should
assume that the competence grammar does not place any restrictions on
scrambling (and that, therefore, all such sentences are in fact grammatical),
and the unacceptability of some (or most) of the grammatically possible word
orders is due to processing limitations. In this paper, we will argue for the
second view by presenting a processing model for German.Comment: 23 pages, uuencoded compressed ps file. In {\em Perspectives on
Sentence Processing}, C. Clifton, Jr., L. Frazier and K. Rayner, editors.
Lawrence Erlbaum Associates, 199
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
Normalized Alignment of Dependency Trees for Detecting Textual Entailment
In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that
normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information
Natural Language Processing
The subject of Natural Language Processing can be considered in both broad and narrow senses. In the broad sense, it covers processing issues at all levels of natural language understanding, including speech recognition, syntactic and semantic analysis of sentences, reference to the discourse context (including anaphora, inference of referents, and more extended relations of discourse coherence and narrative structure), conversational inference and implicature, and discourse planning and generation. In the narrower sense, it covers the syntactic and semantic processing sentences to deliver semantic objects suitable for referring, inferring, and the like. Of course, the results of inference and reference may under some circumstances play a part in processing in the narrow sense. But the processes that are characteristic of these other modules are not the primary concern
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