9,683 research outputs found
Treebank-based acquisition of LFG resources for Chinese
This paper presents a method to automatically acquire wide-coverage, robust, probabilistic Lexical-Functional Grammar resources for Chinese from the Penn Chinese Treebank (CTB). Our starting point is the earlier, proofof-
concept work of (Burke et al., 2004) on automatic f-structure annotation, LFG grammar acquisition and parsing for Chinese using the CTB version 2 (CTB2). We substantially extend and improve on this earlier research as regards coverage, robustness, quality and fine-grainedness of the resulting LFG resources. We achieve this through (i) improved LFG analyses for a number of core Chinese phenomena; (ii) a new automatic f-structure annotation architecture which involves an intermediate dependency representation; (iii) scaling the approach from 4.1K trees in CTB2 to 18.8K trees in CTB version 5.1 (CTB5.1) and (iv) developing a novel treebank-based approach to recovering non-local dependencies (NLDs) for Chinese parser output. Against a new 200-sentence good standard of manually constructed f-structures, the method achieves 96.00% f-score for f-structures automatically generated for the original CTB trees and 80.01%for NLD-recovered f-structures generated for the trees output by Bikel’s parser
Declarative Ajax Web Applications through SQL++ on a Unified Application State
Implementing even a conceptually simple web application requires an
inordinate amount of time. FORWARD addresses three problems that reduce
developer productivity: (a) Impedance mismatch across the multiple languages
used at different tiers of the application architecture. (b) Distributed data
access across the multiple data sources of the application (SQL database, user
input of the browser page, session data in the application server, etc). (c)
Asynchronous, incremental modification of the pages, as performed by Ajax
actions.
FORWARD belongs to a novel family of web application frameworks that attack
impedance mismatch by offering a single unifying language. FORWARD's language
is SQL++, a minimally extended SQL. FORWARD's architecture is based on two
novel cornerstones: (a) A Unified Application State (UAS), which is a virtual
database over the multiple data sources. The UAS is accessed via distributed
SQL++ queries, therefore resolving the distributed data access problem. (b)
Declarative page specifications, which treat the data displayed by pages as
rendered SQL++ page queries. The resulting pages are automatically
incrementally modified by FORWARD. User input on the page becomes part of the
UAS.
We show that SQL++ captures the semi-structured nature of web pages and
subsumes the data models of two important data sources of the UAS: SQL
databases and JavaScript components. We show that simple markup is sufficient
for creating Ajax displays and for modeling user input on the page as UAS data
sources. Finally, we discuss the page specification syntax and semantics that
are needed in order to avoid race conditions and conflicts between the user
input and the automated Ajax page modifications.
FORWARD has been used in the development of eight commercial and academic
applications. An alpha-release web-based IDE (itself built in FORWARD) enables
development in the cloud.Comment: Proceedings of the 14th International Symposium on Database
Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento,
Ital
Gene ranking and biomarker discovery under correlation
Biomarker discovery and gene ranking is a standard task in genomic high
throughput analysis. Typically, the ordering of markers is based on a
stabilized variant of the t-score, such as the moderated t or the SAM
statistic. However, these procedures ignore gene-gene correlations, which may
have a profound impact on the gene orderings and on the power of the subsequent
tests.
We propose a simple procedure that adjusts gene-wise t-statistics to take
account of correlations among genes. The resulting correlation-adjusted
t-scores ("cat" scores) are derived from a predictive perspective, i.e. as a
score for variable selection to discriminate group membership in two-class
linear discriminant analysis. In the absence of correlation the cat score
reduces to the standard t-score. Moreover, using the cat score it is
straightforward to evaluate groups of features (i.e. gene sets). For
computation of the cat score from small sample data we propose a shrinkage
procedure. In a comparative study comprising six different synthetic and
empirical correlation structures we show that the cat score improves estimation
of gene orderings and leads to higher power for fixed true discovery rate, and
vice versa. Finally, we also illustrate the cat score by analyzing metabolomic
data.
The shrinkage cat score is implemented in the R package "st" available from
URL http://cran.r-project.org/web/packages/st/Comment: 18 pages, 5 figures, 1 tabl
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Conditional independence testing is a fundamental problem underlying causal
discovery and a particularly challenging task in the presence of nonlinear and
high-dimensional dependencies. Here a fully non-parametric test for continuous
data based on conditional mutual information combined with a local permutation
scheme is presented. Through a nearest neighbor approach, the test efficiently
adapts also to non-smooth distributions due to strongly nonlinear dependencies.
Numerical experiments demonstrate that the test reliably simulates the null
distribution even for small sample sizes and with high-dimensional conditioning
sets. The test is better calibrated than kernel-based tests utilizing an
analytical approximation of the null distribution, especially for non-smooth
densities, and reaches the same or higher power levels. Combining the local
permutation scheme with the kernel tests leads to better calibration, but
suffers in power. For smaller sample sizes and lower dimensions, the test is
faster than random fourier feature-based kernel tests if the permutation scheme
is (embarrassingly) parallelized, but the runtime increases more sharply with
sample size and dimensionality. Thus, more theoretical research to analytically
approximate the null distribution and speed up the estimation for larger sample
sizes is desirable.Comment: 17 pages, 12 figures, 1 tabl
Permutation Inference for Canonical Correlation Analysis
Canonical correlation analysis (CCA) has become a key tool for population
neuroimaging, allowing investigation of associations between many imaging and
non-imaging measurements. As other variables are often a source of variability
not of direct interest, previous work has used CCA on residuals from a model
that removes these effects, then proceeded directly to permutation inference.
We show that such a simple permutation test leads to inflated error rates. The
reason is that residualisation introduces dependencies among the observations
that violate the exchangeability assumption. Even in the absence of nuisance
variables, however, a simple permutation test for CCA also leads to excess
error rates for all canonical correlations other than the first. The reason is
that a simple permutation scheme does not ignore the variability already
explained by previous canonical variables. Here we propose solutions for both
problems: in the case of nuisance variables, we show that transforming the
residuals to a lower dimensional basis where exchangeability holds results in a
valid permutation test; for more general cases, with or without nuisance
variables, we propose estimating the canonical correlations in a stepwise
manner, removing at each iteration the variance already explained, while
dealing with different number of variables in both sides. We also discuss how
to address the multiplicity of tests, proposing an admissible test that is not
conservative, and provide a complete algorithm for permutation inference for
CCA.Comment: 49 pages, 2 figures, 10 tables, 3 algorithms, 119 reference
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