Results 11 to 20 of about 6,910 (242)
Symbolic Lookaheads for Bottom-up Parsing
, 2016 We present algorithms for the construction of LALR(1) parsing tables, and of LR(1) parsing tables of reduced size. We first define specialized characteristic automata whose states are parametric w.r.t. variables symbolically representing lookahead-sets.
Paola Quaglia
openalex +5 more sources
Bottom-Up/Top-Down Image Parsing with Attribute Grammar [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008 This paper presents a simple attribute graph grammar as a generative representation for made-made scenes, such as buildings, hallways, kitchens, and living rooms, and studies an effective top-down/bottom-up inference algorithm for parsing images in the process of maximizing a Bayesian posterior probability or equivalently minimizing a description ...
Feng Han, Song-Chun Zhu
openalex +5 more sources
A Bottom-Up Parsing Model of Local Coherence Effects [PDF]
, 2010 Human sentence processing occurs incrementally. Most models of human processing rely on parsers that always build connected tree structures. But according to the theory of Good Enough parsing (Ferreira & Patson, 2007), humans parse sentences using small chunks of local information, not alwaysforming a globally coherent parse.
Emily Morgan +2 more
+4 more sources
A new design of prolog-based bottom-up parsing system with Government-Binding theory [PDF]
Proceedings of the 12th conference on Computational linguistics -, 1988 This paper addresses the problems of movement transformation in Prolog-based bottom-up parsing system. Three principles of Government-Binding theory are employed to deal with these problems. They are Empty Category Principle, C-command Principle, and Subjacency Principle. A formalism based upon them is proposed.
Hsin‐Hsi Chen +2 more
openalex +3 more sources
Bottom-Up Recognition and Parsing of the Human Body
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007 Recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. Because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. Previous methods have focused largely on heuristics or pairwise part models in approaching this problem.
P. Srinivasan, Jianbo Shi
+5 more sources
Bottom-up/top-down image parsing by attribute graph grammar [PDF]
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005 In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living moms. We choose one class of primitives - 3D planar rectangles projected on images and six graph grammar production rules.
Feng Han, Song‐Chun Zhu
openalex +2 more sources
On Parsing Programming Languages with Turing-Complete Parser
Mathematics, 2023 A new parsing method based on the semi-Thue system is described. Similar to, but with more efficient implementation than Markov normal algorithms, it can be used for parsing any recursively enumerable language.
Boštjan Slivnik, Marjan Mernik
doaj +1 more source
Frontiers in Systems Neuroscience, 2021 Recent neurocognitive models commonly consider speech perception as a hierarchy of processes, each corresponding to specific temporal scales of collective oscillatory processes in the cortex: 30–80 Hz gamma oscillations in charge of phonetic analysis, 4 ...
Mamady Nabé +3 more
doaj +1 more source
Research on the Top-Down Parsing Method for Context-Sensitive Graph Grammars. [PDF]
PLoS ONE, 2015 The parsing problem is one of the key problems of graph grammars. The typical parsing algorithm uses the bottom-up method. The time-complexity of this method is high, and it is difficult to apply.
Yi Wang, XiaoQin Zeng, Han Ding
doaj +1 more source
Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model
IEEE Access, 2023 Human pose estimation has drawn extensive attention recently and there has been significant progress on it due to the rising popularity of convolutional neural networks (CNN).
Shuhena Salam Aonty +4 more
doaj +1 more source