2,280 research outputs found
Semantic Image Segmentation via Deep Parsing Network
This paper addresses semantic image segmentation by incorporating rich
information into Markov Random Field (MRF), including high-order relations and
mixture of label contexts. Unlike previous works that optimized MRFs using
iterative algorithm, we solve MRF by proposing a Convolutional Neural Network
(CNN), namely Deep Parsing Network (DPN), which enables deterministic
end-to-end computation in a single forward pass. Specifically, DPN extends a
contemporary CNN architecture to model unary terms and additional layers are
carefully devised to approximate the mean field algorithm (MF) for pairwise
terms. It has several appealing properties. First, different from the recent
works that combined CNN and MRF, where many iterations of MF were required for
each training image during back-propagation, DPN is able to achieve high
performance by approximating one iteration of MF. Second, DPN represents
various types of pairwise terms, making many existing works as its special
cases. Third, DPN makes MF easier to be parallelized and speeded up in
Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC
2012 dataset, where a single DPN model yields a new state-of-the-art
segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201
Efficient Algorithms for Parsing the DOP Model
Excellent results have been reported for Data-Oriented Parsing (DOP) of
natural language texts (Bod, 1993). Unfortunately, existing algorithms are both
computationally intensive and difficult to implement. Previous algorithms are
expensive due to two factors: the exponential number of rules that must be
generated and the use of a Monte Carlo parsing algorithm. In this paper we
solve the first problem by a novel reduction of the DOP model to a small,
equivalent probabilistic context-free grammar. We solve the second problem by a
novel deterministic parsing strategy that maximizes the expected number of
correct constituents, rather than the probability of a correct parse tree.
Using the optimizations, experiments yield a 97% crossing brackets rate and 88%
zero crossing brackets rate. This differs significantly from the results
reported by Bod, and is comparable to results from a duplication of Pereira and
Schabes's (1992) experiment on the same data. We show that Bod's results are at
least partially due to an extremely fortuitous choice of test data, and
partially due to using cleaner data than other researchers.Comment: 10 page
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
Robust Grammatical Analysis for Spoken Dialogue Systems
We argue that grammatical analysis is a viable alternative to concept
spotting for processing spoken input in a practical spoken dialogue system. We
discuss the structure of the grammar, and a model for robust parsing which
combines linguistic sources of information and statistical sources of
information. We discuss test results suggesting that grammatical processing
allows fast and accurate processing of spoken input.Comment: Accepted for JNL
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