Distributional Learning of Some Context-Free Languages with a Minimally Adequate Teacher

2010 ◽  
pp. 24-37 ◽  
Author(s):  
Alexander Clark
Keyword(s):  
Distributional Learning ◽  
Context Free

scholarly journals Distributional learning of parallel multiple context-free grammars

2013 ◽  
Vol 96 (1-2) ◽  
pp. 5-31 ◽  
Author(s):  
Alexander Clark ◽  
Ryo Yoshinaka
Keyword(s):  
Multiple Context ◽  
Distributional Learning ◽  
Context Free ◽  
Context Free Grammars

scholarly journals Consistent Unsupervised Estimators for Anchored PCFGs

2020 ◽  
Vol 8 ◽  
pp. 409-422
Author(s):  
Alexander Clark ◽  
Nathanaël Fijalkow
Keyword(s):  
Computational Linguistics ◽  
Synthetic Data ◽  
Finite Sample ◽  
Natural Conditions ◽  
Classic Problem ◽  
Distributional Properties ◽  
Distributional Learning ◽  
Naive Estimator ◽  
Context Free ◽  
Probabilistic Context

Learning probabilistic context-free grammars (PCFGs) from strings is a classic problem in computational linguistics since Horning ( 1969 ). Here we present an algorithm based on distributional learning that is a consistent estimator for a large class of PCFGs that satisfy certain natural conditions including being anchored (Stratos et al., 2016 ). We proceed via a reparameterization of (top–down) PCFGs that we call a bottom–up weighted context-free grammar. We show that if the grammar is anchored and satisfies additional restrictions on its ambiguity, then the parameters can be directly related to distributional properties of the anchoring strings; we show the asymptotic correctness of a naive estimator and present some simulations using synthetic data that show that algorithms based on this approach have good finite sample behavior.

Distributional Learning in Categorization of Speech and Non-Speech Sounds

2012 ◽  
Author(s):  
Megan M. Kittleson ◽  
Jessamyn Schertz ◽  
Randy Diehl ◽  
Andrew J. Lotto
Keyword(s):  
Speech Sounds ◽  
Distributional Learning

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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