Clustering Morphological Paradigms Using Syntactic Categories

Can, Burcu; Manandhar, Suresh

doi:10.1007/978-3-642-15754-7_77

Cited by 9 publications

(9 citation statements)

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“…Part of speech information can also be useful, and algorithms have been developed for induction of distributional word classes through sentential contexts (Schütze 1993;Redington et al 1998). There have been efforts to combine morphology induction and distributional induction of word classes (Parkes et al 1998;Wicentowski 2002;Clark 2003;Higgins 2003;Freitag 2004Freitag , 2005Hu et al 2005b;Biemann 2006;Dasgupta and Ng 2007;Can and Manandhar 2009). …”

Section: Unsupervised Induction Of Morphology and Part Of Speech Classesmentioning

confidence: 99%

Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning

Chan

Lignos

2010

Res on Lang and Comput

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We develop an unsupervised algorithm for morphological acquisition to investigate the relationship between linguistic representation, data statistics, and learning algorithms. We model the phenomenon that children acquire the morphological inflections of a language monotonically by introducing an algorithm that uses a bootstrapped, frequency-driven learning procedure to acquire rules monotonically. The algorithm learns a morphological grammar in terms of a Base and Transforms representation, a simple rule-based model of morphology. When tested on corpora of child-directed speech in English from CHILDES (MacWhinney in The CHILDESProject: Tools for analyzing talk. Erlbaum, Hillsdale, 2000), the algorithm learns the most salient rules of English morphology and the order of acquisition is similar to that of children as observed by Brown (A first language: the early stages. Harvard University Press, Cambridge, 1973). Investigations of statistical distributions in corpora reveal that the algorithm is able to acquire morphological grammars due to its exploitation of Zipfian distributions in morphology through type-frequency statistics. These investigations suggest that the computation and frequency-driven selection of discrete morphological rules may be important factors in children's acquisition of basic inflectional morphological systems.

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Section: Unsupervised Induction Of Morphology and Part Of Speech Classesmentioning

confidence: 99%

Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning

Chan

Lignos

2010

Res on Lang and Comput

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“…The system outperforms other unsupervised morphological segmentation systems that competed in Morpho Challenge 2009 (Kurimo et al 2009) for the languages Finnish, Turkish, and German. Can and Manandhar (2010) exploit syntactic categories to capture morphological paradigms. In a deterministic scheme, morphological paradigms are learned by pairing syntactic categories and identifying common suffixes between them.…”

Section: Related Workmentioning

confidence: 99%

Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation

Can

Manandhar

2018

Computational Linguistics

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This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.

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“…Lignos [53] develops an inference procedure that can learn the base form of a word when it is absent in the corpus. Can and Manandhar [14] propose a deterministic model that makes use of syntactic categories. Syntactic information and morphology are strongly connected to each other.…”

Section: Wordsmentioning

confidence: 99%

“…-Letter Successor Variety models: Harris [39], Hafer and Weiss [36], Dejean [26], Bordag [9,10] -Minimum Description Length based models: Brent et al [12], Goldsmith's Linguistica [30,31], Morfessor Baseline MDL [22], Argamon et al [1], Kazakov & Manandhar [42,43] -Other deterministic approaches: Bernhard [5], Neuvel and Fulow [60], Keshava and Pitler [44], Monson et al [57], Lignos et al [54], Can and Manandhar [14] -Maximum likelihood models: Morfessor Baseline ML [22], Morfessor Categories ML [23], Probabilistic ParaMor [58] -Maximum A-Posteriori models: Morfessor Categories MAP [24] -Bayesian parametric models: Creutz [19], Poon et al [62] -Bayesian non-parametric models: Goldwater et al [32], Can and Manandhar [15], Sirts and Alumäe [67], Dreyer and Eisner [28], Snyder and Barzilay [69] 2 Related work…”

Section: Introductionmentioning

confidence: 99%

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Methods and Algorithms for Unsupervised Learning of Morphology

Can

Manandhar

2014

Computational Linguistics and Intelligent Text Processing

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This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.

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Clustering Morphological Paradigms Using Syntactic Categories

Cited by 9 publications

References 10 publications

Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning

Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning

Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation

Methods and Algorithms for Unsupervised Learning of Morphology

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