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A Spectral Approach for Probabilistic Grammatical Inference on Trees [chapter]

Raphaël Bailly, Amaury Habrard, François Denis
2010 Lecture Notes in Computer Science  
We focus on the estimation of a probability distribution over a set of trees.  ...  We consider here the class of distributions computed by weighted automata -a strict generalization of probabilistic tree automata.  ...  Acknowledgement The authors wish to thank the anonymous reviewers for their comments and suggestions.  ... 
doi:10.1007/978-3-642-16108-7_10 fatcat:36olh4f63jhk7klwtc7puaiute

Guest editors' introduction to the special section on syntactic and structural pattern recognition

M. Basu, H. Bunke, A. Del Bimbo
2005 IEEE Transactions on Pattern Analysis and Machine Intelligence  
She is the guest editor of a special issue on grammatical inference techniques and applications that will appear in Pattern Recognition Journal in Alberto Del Bimbo is a full professor of computer engineering  ...  Probabilistic finite state machines represent a class of syntactic objects, e.g., probabilistic finite state automata, hidden Markov models, stochastic regular grammars, probabilistic suffix trees, n-grams  ... 
doi:10.1109/tpami.2005.141 pmid:16013749 fatcat:pfxujyxazjdkxfku6ymwjy3dfe

Spectral learning of weighted automata

Borja Balle, Xavier Carreras, Franco M. Luque, Ariadna Quattoni
2013 Machine Learning  
One of the goals of this paper is to remedy this situation by presenting a derivation of the spectral method for learning WFA that-without sacrificing rigor and mathematical elegance-puts emphasis on providing  ...  To illustrate the approach we present experiments on a real application of the method to natural language parsing.  ...  Acknowledgements We are grateful to the anonymous reviewers for providing us with helpful comments. This work was supported by a Google Research Award, and by projects XLike (FP7-288342), BASMATI  ... 
doi:10.1007/s10994-013-5416-x fatcat:gdkrhg3qpvcvzchkbwuw62j6ja

Statistical methods in language processing

Steven Abney
2010 Wiley Interdisciplinary Reviews: Cognitive Science  
Standard hypothesis testing and experimental design, for example, are not covered in this article.   ...  Statistical methods have so thoroughly permeated computational linguistics that almost all work in the field draws on them in some way.  ...  Stochastic attribute-value grammars are based on random fields. 32 Probabilistic versions of other context-sensitive formalisms, such as Tree Adjoining Grammar, have also been developed. 33 Grammatical  ... 
doi:10.1002/wcs.111 pmid:26302079 fatcat:qnockuwjdzagxjgjqev36kwsee

PAutomaC: a probabilistic automata and hidden Markov models learning competition

Sicco Verwer, Rémi Eyraud, Colin de la Higuera
2013 Machine Learning  
The Probabilistic Automata learning Competition (PAutomaC), run in 2012, was the first grammatical inference challenge that allowed the comparison between these methods and algorithms.  ...  This paper also provides the results of the competition and a brief description and analysis of the different approaches the main participants used.  ...  Acknowledgements We are very thankful to the members of the scientific committee for their help in designing this competition.  ... 
doi:10.1007/s10994-013-5409-9 fatcat:ey3ghvxqxzbevmpzxmv5tytvay

Backdoors in Neural Models of Source Code [article]

Goutham Ramakrishnan, Aws Albarghouthi
2020 arXiv   pre-print
and improve recent algorithms from robust statistics for our setting, showing that backdoors leave a spectral signature in the learned representation of source code, thus enabling detection of poisoned  ...  An attacker can implant a backdoor by poisoning the training data to yield a desired target prediction on triggered inputs.  ...  Second, grammatical triggers add pieces of dead code drawn randomly from some probabilistic grammar. So a grammatical trigger t is a randomized operation.  ... 
arXiv:2006.06841v1 fatcat:itdhfdeg2fgnzgk432uz6qlhwu

Review of: Speech and language processing

Sheila Garfield
2001 Cognitive Systems Research  
Regular models of phonologithe book is highly recommended for all involved in cal rule systems. Computational Linguistics 20 (3)  ...  Also under discussion are probabilistic lexparse trees are supported diagrammatically.  ...  notion of spectral features, the State Transducers.  ... 
doi:10.1016/s1389-0417(01)00022-5 fatcat:cxemu3sqbjfmlnk5wlhrpspguq

Inductive Logic and Empirical Psychology [chapter]

Nick Chater, Mike Oaksford, Ulrike Hahn, Evan Heit
2011 Handbook of the History of Logic  
ACKNOWLEDGEMENTS Acknowledgements: Nick Chater is supported by a Senior Research Fellowship from the Leverhulme Trust, and the ESRC Centre for Economic Learning and Social Evolution (ELSE).  ...  perspective on probabilistic inference.  ...  A Probabilistic Approach In empirical psychology, there are a variety of probabilistic approaches to conditional inference [Anderson, 1995; Liu, 2003; Evans and Over, 2004; Pfeifer and Kleiter, 2005;  ... 
doi:10.1016/b978-0-444-52936-7.50014-8 fatcat:ex776zoztrd63nva3rjuadzfje

Learning Probability Distributions Generated by Finite-State Machines [chapter]

Jorge Castro, Ricard Gavaldà
2016 Topics in Grammatical Inference  
The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata.  ...  We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation.  ...  We thank the chairs of ICGI 2012 for the invitation to present a preliminary version of this work as tutorial. We particularly thank the reviewer of this version for a thorough and useful work.  ... 
doi:10.1007/978-3-662-48395-4_5 fatcat:u4cepbpghjcv7ct6zoqrgir2cy

Automatic recognition and understanding of spoken language - a first step toward natural human-machine communication

Bing-Hwang Juang, S. Furui
2000 Proceedings of the IEEE  
In this paper, we summarize the development of the spoken language technology from both a vertical (the chronology) and a horizontal (the spectrum of technical approaches) perspective.  ...  Today, research results in spoken language processing have led to a number of successful applications, ranging from dictation software for personal computers and telephone-call processing systems for automatic  ...  probabilistic FSG, and so on.  ... 
doi:10.1109/5.880077 fatcat:6ca4ebtwcbg4tl6bgcvgtr2gry

Probabilistic finite-state machines - part II

E. Vidal, F. Thollard, C. de la Higuera, F. Casacuberta, R.C. Carrasco
2005 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked.  ...  , and properties that represent a current state of the art of these objects.  ...  ACKNOWLEDGMENTS The authors wish to thank the anonymous reviewers for their careful reading and in-depth criticisms and suggestions.  ... 
doi:10.1109/tpami.2005.148 pmid:16013757 fatcat:vaoopt4ypzffzpv53pxx2hodpy

Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference [article]

Amaury Habrard
2008 arXiv   pre-print
In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language.  ...  The estimate of P stands in some class of probabilistic models such as probabilistic automata (PA). In this paper, we focus on probabilistic models based on multiplicity automata (MA).  ...  Moreover, our experiments showed that DEES outperforms standard probabilistic grammat-ical inference approaches.  ... 
arXiv:cs/0607085v2 fatcat:2x3xe6svgfdevg67o2m6mwf4pi

Probabilistic Grammars and their Applications [chapter]

S. Geman, M. Johnson
2001 International Encyclopedia of the Social & Behavioral Sciences  
As explained in the body of this paper, stochastic approaches replace the binary distinctions (grammatical versus un-grammatical) of non-stochastic approaches with probability distributions.  ...  We will review the main probabilistic grammars and their associated theories of inference.  ... 
doi:10.1016/b0-08-043076-7/00489-7 fatcat:tkff6uyc6vc3nl3wdqgfiwnkpu

Probabilistic Grammars and their Applications [chapter]

Stuart Geman, Mark Johnson
2015 International Encyclopedia of the Social & Behavioral Sciences  
As explained in the body of this paper, stochastic approaches replace the binary distinctions (grammatical versus un-grammatical) of non-stochastic approaches with probability distributions.  ...  We will review the main probabilistic grammars and their associated theories of inference.  ... 
doi:10.1016/b978-0-08-097086-8.42161-6 fatcat:sqtjyijpivcq7kylqg7q2qdiuy

Editors' Introduction [chapter]

Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
2013 Lecture Notes in Computer Science  
In his invited talk Towards General Algorithms for Grammatical Inference, Alexander Clark deals with the learning of context-free languages and multiple context-free languages.  ...  approaches.  ...  In their paper A Spectral Approach for Probabilistic Grammatical Inference of Trees, Raphaël Bally, François Denis and Amaury Habrard consider distributions over the set of trees which are computed by  ... 
doi:10.1007/978-3-642-40935-6_1 fatcat:pchrsvhjezfbvh6dfplqhxhgcy
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