The String-to-String Correction Problem
Journal of the ACM (JACM)
A minimum description length approach to grammar inference
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Memory-Based Lexical Acquisition and Processing
Proceedings of the Third International EAMT Workshop on Machine Translation and the Lexicon
Unsupervised language acquisition
Unsupervised language acquisition
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
A DOP model for semantic interpretation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Bayesian grammar induction for language modeling
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Unsupervised induction of stochastic context-free grammars using distributional clustering
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Inducing probabilistic invertible translation grammars from aligned texts
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Experiments in parallel-text based grammar induction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
An all-subtrees approach to unsupervised parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Artificial Intelligence
Automatic Polyphonic Music Composition Using the EMILE and ABL Grammar Inductors
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
History-Based Inside-Outside Algorithm
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Unsupervised Grammar Induction Using a Parent Based Constituent Context Model
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Unsupervised parsing with U-DOP
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Learning auxiliary fronting with grammatical inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The SED heuristic for morpheme discovery: a look at Swahili
PMHLA '05 Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition
Learning Relational Grammars from Sequences of Actions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Grammatical inference and computational linguistics
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
Natural language grammar induction with a generative constituent-context model
Pattern Recognition
On the usage of morphological tags for grammar induction
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
SSGL: a semi-supervised grammar learner
International Journal of Computer Applications in Technology
Bounding the maximal parsing performance of non-terminally separated grammars
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Simple unsupervised grammar induction from raw text with cascaded finite state models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A comparative study on chinese word clustering
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Variational bayesian grammar induction for natural language
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Inferring grammar rules of programming language dialects
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Computational models of language acquisition
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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This paper introduces a new type of grammar learning algorithm, inspired by string edit distance (Wagner and Fischer, 1974). The algorithm takes a corpus of flat sentences as input and returns a corpus of labelled, bracketed sentences. The method works on pairs of unstructured sentences that have one or more words in common. When two sentences are divided into parts that are the same in both sentences and parts that are different, this information is used to find parts that are interchangeable. These parts are taken as possible constituents of the same type. After this alignment learning step, the selection learning step selects the most probable constituents from all possible constituents.This method was used to bootstrap structure on the ATIS corpus (Marcus et. al., 1993) and on the OVIS! corpus (Bonnema et al., 1997). While the results are encouraging (we obtained up to 89.25% non-crossing brackets precision), this paper will point out some of the shortcomings of our approach and will suggest possible solutions.