Results 201 to 210 of about 6,910 (242)

Producing a top-down parse order with bottom-up parsing

Information Processing Letters, 1995
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Schmeiser, James P., Barnard, David T.
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Language modeling using efficient best-first bottom-up parsing

2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), 2004
In this paper we present a two-stage best-first bottom-up word-lattice parser which we use as a language model for speech recognition. The parser works by using a "figure of merit" that selects lattice paths while simultaneously selecting syntactic category edges for parsing. Additionally, we introduce a modified version of the inside-outside algorithm
K. Hall, M. Johnson
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Toward a Bottom-Up Parsing Virtual Machine

2010 Second International Conference on Computer Engineering and Applications, 2010
This paper proposes a virtual machine specialized for bottom-up parsing. All existing parser generators are tightly coupled to their target programming languages. In this paper’s approach, generated parsers are written in machine code for a virtual machine. They are loosely coupled to any programming languages.
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A bottom‐up parsing method for complete context‐free tree languages

Systems and Computers in Japan, 1986
AbstractThis paper proposes a bottom‐up parsing method for complete context‐free tree grammar. The complete context‐free tree grammar is an extension both of one‐dimensional context‐free grammar and regular tree grammar. This parsing method is an extension of the parsing method of Cocke‐Kasami‐Younger for one‐dimensional context‐free languages, and is ...
Kyota Aoki, Kazumi Matsuura
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Parallel Bottom-up Tomita parsing

1997
In the previous chapter we have derived the parsing schema LR(0) and concluded that the differences with Earley are trivial details. Hence there is a structural correspondence between Earley chart parser and generalized LR parsers. This correspondence can be used to cross-fertilize different variants of either kind algorithm. A particularly interesting
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A parsing method for context‐free languages using bottom‐up lookahead computation

Systems and Computers in Japan, 1996
AbstractA new parsing algorithm of context‐free languages using bottom‐up computation of lookahead information is presented. Earley's algorithm uses a great number of items during enumeration to recognize a context‐free language. If items with lookahead fields are used, the number of items can be reduced, but item spacing is considerably increased. The
Yoshimichi Watanabe, Takehiro Tokuda
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Incremental learning of context free grammars based on bottom-up parsing and search

Pattern Recognition, 2005
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Nakamura, Katsuhiko, Matsumoto, Masashi
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RETE and chart parsing from bottom-up call-graph caching

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91, 2002
A new mechanism is presented for graph instantiation. It is used to investigate various bottom-up data-driven AI algorithms. The author views instances as fairly autonomous objects that inherit graph information (e.g. links or relations) from their classes. Instances themselves can be instantiated.
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Accurate 3D bone segmentation in challenging CT images: Bottom-up parsing and contextualized optimization

2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016
In full or arbitrary field-of-view (FOV) 3D CT imaging, obtaining an accurate per-voxel segmentation for complete large and small bones remains an unsolved and challenging problem. The difficulty lies in the notable variation in appearance and position observed among cortical bones, marrow and pathologies. To approach this problem, several studies have
Le Lu, Dijia Wu, Nathan Lay, David Liu, Isabella Nogues, Ronald M. Summers   +5 more
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On the Inference of Context-Free Grammars Based On Bottom-Up Parsing and Search

2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009
We consider the problem of incremental learning of context-free grammars, using inductive CYK (Cocke-YoungerKasami) algorithm [4], based on the non-deterministic learning scheme proposed by Nakamura and Matsumoto in 2005 [1]. We implement their learning scheme deterministically and illustrate several examples in order to understand the incremental ...
Gend Lal Prajapati, Aditya Jain, Mayank Khandelwal, Pooja Nema, Priyanka Shukla   +4 more
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