19,299 research outputs found
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a -best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision from annotated data and the flexibility to use newly annotated
(possibly uncommon) images, and present a quasi-parametric human parsing model.
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the
parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict
the matching confidence and displacements of the best matched region in the
testing image for a particular semantic region in one KNN image. Given a
testing image, we first retrieve its KNN images from the
annotated/manually-parsed human image corpus. Then each semantic region in each
KNN image is matched with confidence to the testing image using M-CNN, and the
matched regions from all KNN images are further fused, followed by a superpixel
smoothing procedure to obtain the ultimate human parsing result. The M-CNN
differs from the classic CNN in that the tailored cross image matching filters
are introduced to characterize the matching between the testing image and the
semantic region of a KNN image. The cross image matching filters are defined at
different convolutional layers, each aiming to capture a particular range of
displacements. Comprehensive evaluations over a large dataset with 7,700
annotated human images well demonstrate the significant performance gain from
the quasi-parametric model over the state-of-the-arts, for the human parsing
task.Comment: This manuscript is the accepted version for CVPR 201
Parsing as Reduction
We reduce phrase-representation parsing to dependency parsing. Our reduction
is grounded on a new intermediate representation, "head-ordered dependency
trees", shown to be isomorphic to constituent trees. By encoding order
information in the dependency labels, we show that any off-the-shelf, trainable
dependency parser can be used to produce constituents. When this parser is
non-projective, we can perform discontinuous parsing in a very natural manner.
Despite the simplicity of our approach, experiments show that the resulting
parsers are on par with strong baselines, such as the Berkeley parser for
English and the best single system in the SPMRL-2014 shared task. Results are
particularly striking for discontinuous parsing of German, where we surpass the
current state of the art by a wide margin
Parsing coordinations
The present paper is concerned with statistical parsing of constituent structures in German. The paper presents four experiments that aim at improving parsing performance of coordinate structure: 1) reranking the n-best parses of a PCFG parser, 2) enriching the input to a PCFG parser by gold scopes for any conjunct, 3) reranking the parser output for all possible scopes for conjuncts that are permissible with regard to clause structure. Experiment 4 reranks a combination of parses from experiments 1 and 3. The experiments presented show that n- best parsing combined with reranking improves results by a large margin. Providing the parser with different scope possibilities and reranking the resulting parses results in an increase in F-score from 69.76 for the baseline to 74.69. While the F-score is similar to the one of the first experiment (n-best parsing and reranking), the first experiment results in higher recall (75.48% vs. 73.69%) and the third one in higher precision (75.43% vs. 73.26%). Combining the two methods results in the best result with an F-score of 76.69
Edge-Based Best-First Chart Parsing
Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged 'best' by some probabilistic figure of merit (FOM). Recent work has used proba- bilistic context-free grammars (PCFGs) to sign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to us- ing a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results -- that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing
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