Guessing morphology from terms and corpora
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus-based stemming using cooccurrence of word variants
ACM Transactions on Information Systems (TOIS)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Knowledge-free induction of morphology using latent semantic analysis
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Morphemes as necessary concept for structures discovery from untagged corpora
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
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Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
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Proceedings of the 2005 ACM symposium on Applied computing
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ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Unsupervised learning of morphology without morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Modeling english past tense intuitions with minimal generalization
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Using 'smart' bilingual projection to feature-tag a monolingual dictionary
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Unsupervised models for morpheme segmentation and morphology learning
ACM Transactions on Speech and Language Processing (TSLP)
A framework for unsupervised natural language morphology induction
ACLstudent '04 Proceedings of the ACL 2004 workshop on Student research
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ACM Transactions on Speech and Language Processing (TSLP)
An unsupervised Hindi stemmer with heuristic improvements
Proceedings of the second workshop on Analytics for noisy unstructured text data
Unsupervised morphological segmentation with log-linear models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Induction of a simple morphology for highly-inflecting languages
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
Unsupervised induction of natural language morphology inflection classes
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
Multilingual noise-robust supervised morphological analysis using the WordFrame model
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
Morphology induction from limited noisy data using approximate string matching
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Learning probabilistic paradigms for morphology in a latent class model
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
A naive theory of affixation and an algorithm for extraction
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Evaluating an agglutinative segmentation model for ParaMor
SigMorPhon '08 Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology
ParaMor: minimally supervised induction of paradigm structure and morphological analysis
SigMorPhon '07 Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology
Unsupervised morphological segmentation and clustering with document boundaries
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Morphology induction from term clusters
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Inducing Morphemes Using Light Knowledge
ACM Transactions on Asian Language Information Processing (TALIP)
ParaMor and Morpho challenge 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Predicting the semantic compositionality of prefix verbs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Generating learner-like morphological errors in Russian
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
EMMA: a novel Evaluation Metric for Morphological Analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Research on Language and Computation
Universal morphological analysis using structured nearest neighbor prediction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Discovering morphological paradigms from plain text using a Dirichlet process mixture model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Arabic retrieval revisited: morphological hole filling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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We propose an algorithm to automatically induce the morphology of inflectional languages using only text corpora and no human input. Our algorithm combines cues from orthography, semantics, and syntactic distributions to induce morphological relationships in German, Dutch, and English. Using CELEX as a gold standard for evaluation, we show our algorithm to be an improvement over any knowledge-free algorithm yet proposed.