323 research outputs found
On Hilberg's Law and Its Links with Guiraud's Law
Hilberg (1990) supposed that finite-order excess entropy of a random human
text is proportional to the square root of the text length. Assuming that
Hilberg's hypothesis is true, we derive Guiraud's law, which states that the
number of word types in a text is greater than proportional to the square root
of the text length. Our derivation is based on some mathematical conjecture in
coding theory and on several experiments suggesting that words can be defined
approximately as the nonterminals of the shortest context-free grammar for the
text. Such operational definition of words can be applied even to texts
deprived of spaces, which do not allow for Mandelbrot's ``intermittent
silence'' explanation of Zipf's and Guiraud's laws. In contrast to
Mandelbrot's, our model assumes some probabilistic long-memory effects in human
narration and might be capable of explaining Menzerath's law.Comment: To appear in Journal of Quantitative Linguistic
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
Global Thresholding and Multiple Pass Parsing
We present a variation on classic beam thresholding techniques that is up to
an order of magnitude faster than the traditional method, at the same
performance level. We also present a new thresholding technique, global
thresholding, which, combined with the new beam thresholding, gives an
additional factor of two improvement, and a novel technique, multiple pass
parsing, that can be combined with the others to yield yet another 50%
improvement. We use a new search algorithm to simultaneously optimize the
thresholding parameters of the various algorithms.Comment: Fixed latex errors; fixed minor errors in published versio
Computation of distances for regular and context-free probabilistic languages
Several mathematical distances between probabilistic languages have been investigated in the literature, motivated by applications in language modeling, computational biology, syntactic pattern matching and machine learning. In most cases, only pairs of probabilistic regular languages were considered. In this paper we extend the previous results to pairs of languages generated by a probabilistic context-free grammar and a probabilistic finite automaton.PostprintPeer reviewe
Stochastic Attribute-Value Grammars
Probabilistic analogues of regular and context-free grammars are well-known
in computational linguistics, and currently the subject of intensive research.
To date, however, no satisfactory probabilistic analogue of attribute-value
grammars has been proposed: previous attempts have failed to define a correct
parameter-estimation algorithm.
In the present paper, I define stochastic attribute-value grammars and give a
correct algorithm for estimating their parameters. The estimation algorithm is
adapted from Della Pietra, Della Pietra, and Lafferty (1995). To estimate model
parameters, it is necessary to compute the expectations of certain functions
under random fields. In the application discussed by Della Pietra, Della
Pietra, and Lafferty (representing English orthographic constraints), Gibbs
sampling can be used to estimate the needed expectations. The fact that
attribute-value grammars generate constrained languages makes Gibbs sampling
inapplicable, but I show how a variant of Gibbs sampling, the
Metropolis-Hastings algorithm, can be used instead.Comment: 23 pages, 21 Postscript figures, uses rotate.st
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