27,760 research outputs found
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
Patterns in syntactic dependency networks
Many languages are spoken on Earth. Despite their diversity, many robust language universals are known to exist. All languages share syntax, i.e., the ability of combining words for forming sentences. The origin of such traits is an issue of open debate. By using recent developments from the statistical physics of complex networks, we show that different syntactic dependency networks (from Czech, German, and Romanian) share many nontrivial statistical patterns such as the small world phenomenon, scaling in the distribution of degrees, and disassortative mixing. Such previously unreported features of syntax organization are not a trivial consequence of the structure of sentences, but an emergent trait at the global scale.Peer ReviewedPostprint (published version
How do individual cognitive differences relate to acceptability judgments?: A reply to Sprouse, Wagers, and Phillips
Sprouse, Wagers, and Phillips (2012) carried out two experiments in which they measured individual differences in memory to test processing accounts of island effects. They found that these individual differences failed to predict the magnitude of island effects, and they construe these findings as counterevidence to processing-based accounts of island effects. Here, we take up several problems with their methods, their findings, and their conclusions.
First, the arguments against processing accounts are based on null results using tasks that may be ineffective or inappropriate measures of working memory (the n-back and serial-recall tasks). The authors provide no evidence that these two measures predict judgments for other constructions that are difficult to process and yet are clearly grammatical. They assume that other measures of working memory would have yielded the same result, but provide no justification that they should. We further show that whether a working-memory measure relates to judgments of grammatical, hard-to-process sentences depends on how difficult the sentences are. In this light, the stimuli used by the authors present processing difficulties other than the island violations under investigation and may have been particularly hard to process. Second, the Sprouse et al. results are statistically in line with the hypothesis that island sensitivity varies with working memory. Three out of the four island types in their experiment 1 show a significant relation between memory scores and island sensitivity, but the authors discount these findings on the grounds that the variance accounted for is too small to have much import. This interpretation, however, runs counter to standard practices in linguistics, psycholinguistics, and psychology
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Erratum to: Experimental syntax and the variation of island effects in English and Italian, Nat Lang Linguist Theory, (2015), 10.1007/s11049-015-9286-8
Discovery of Linguistic Relations Using Lexical Attraction
This work has been motivated by two long term goals: to understand how humans
learn language and to build programs that can understand language. Using a
representation that makes the relevant features explicit is a prerequisite for
successful learning and understanding. Therefore, I chose to represent
relations between individual words explicitly in my model. Lexical attraction
is defined as the likelihood of such relations. I introduce a new class of
probabilistic language models named lexical attraction models which can
represent long distance relations between words and I formalize this new class
of models using information theory.
Within the framework of lexical attraction, I developed an unsupervised
language acquisition program that learns to identify linguistic relations in a
given sentence. The only explicitly represented linguistic knowledge in the
program is lexical attraction. There is no initial grammar or lexicon built in
and the only input is raw text. Learning and processing are interdigitated. The
processor uses the regularities detected by the learner to impose structure on
the input. This structure enables the learner to detect higher level
regularities. Using this bootstrapping procedure, the program was trained on
100 million words of Associated Press material and was able to achieve 60%
precision and 50% recall in finding relations between content-words. Using
knowledge of lexical attraction, the program can identify the correct relations
in syntactically ambiguous sentences such as ``I saw the Statue of Liberty
flying over New York.''Comment: dissertation, 56 page
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
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