The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
S R T Kudri - One of the best experts on this subject based on the ideXlab platform.
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Countability in l fuzzy topology
Fuzzy Sets and Systems, 1995Co-Authors: S R T KudriAbstract:Abstract Good definitions of L -fuzzy countable and sequential compactness and the Lindelof property are presented for L a fuzzy lattice. Each of them is defined on arbitrary L -fuzzy sets and their properties studied.
Timothy Baldwin - One of the best experts on this subject based on the ideXlab platform.
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the ins and outs of dutch noun Countability classification
Proceedings of the Australasian Language Technology Workshop 2003, 2003Co-Authors: Timothy Baldwin, Leonoor Van Der BeekAbstract:This paper presents a range of methods for classifying Dutch noun Countability based on either Dutch or English data. The classification is founded on translational equivalences and the corpus analysis of linguistic features which correlate with particular Countability classes. We show that crosslingual classification on the basis of word-to-word or featureto-feature mappings between English and Dutch performs at least as well as in-language classification based on gold-standard Dutch Countability data.
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a plethora of methods for learning english Countability
Empirical Methods in Natural Language Processing, 2003Co-Authors: Timothy Baldwin, Francis BondAbstract:This paper compares a range of methods for classifying words based on linguistic diagnostics, focusing on the task of learning countabilities for English nouns. We propose two basic approaches to feature representation: distribution-based representation, which simply looks at the distribution of features in the corpus data, and agreement-based representation which analyses the level of token-wise agreement between multiple preprocessor systems. We additionally compare a single multiclass classifier architecture with a suite of binary classifiers, and combine analyses from multiple preprocessors. Finally, we present and evaluate a feature selection method.
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learning the Countability of english nouns from corpus data
Meeting of the Association for Computational Linguistics, 2003Co-Authors: Timothy Baldwin, Francis BondAbstract:This paper describes a method for learning the Countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 Countability classes. We were able to assign Countability to English nouns with a precision of 94.6%.
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crosslingual Countability classification with eurowordnet
Computational Linguistics in the Netherlands, 2003Co-Authors: Leonoor Van Der Beek, Timothy BaldwinAbstract:We examine the hypothesis that noun Countability is consistent for a given word semantics by way of a series of experiments involving EuroWordNet and the English and Dutch languages. The basic method involves determining a default set of countabilities for each EuroWordNet synset based on Countability-mapped words in that synset, and testing the match between these countabilities and those of held-out words. As EuroWordNet provides crosslingual synset correspondences between Dutch and English, we are able to evaluate the method both monolingually for Dutch and English, and crosslingually between the two languages. We found that Dutch and English countabilities align as well cross-lingually as they do monolingually.
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crosslingual Countability classification english meets dutch
2003Co-Authors: Leonoor Van Der Beek, Timothy BaldwinAbstract:This paper presents a range of methods for classifying Dutch nouns as countable, uncountable or plural only based on both Dutch and English data. The classification is based on the occurrence of Countability specific linguistic features that are extracted from unannotated corpora. We show that in the absence of reliable Dutch gold standard data, cross-linguistic classification can be achieved on the basis of a word-toword or feature-to-feature mapping between English and Dutch.
Fang Jinming - One of the best experts on this subject based on the ideXlab platform.
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Countability axioms in i fuzzy topological spaces
Fuzzy Sets and Systems, 2006Co-Authors: Li Qinghua, Fang JinmingAbstract:In this paper, we introduce the concepts of first Countability, second Countability, density, separability, and Lindelof property for I-fuzzy topological spaces, and study their properties and the relationships between them. Furthermore, we give the Lindelof theorem in I-fuzzy topological spaces and describe fuzzy continuous maps by convergence of sequences. Finally, we discuss the productive property of first Countability of I-fuzzy topological spaces.
Li Qinghua - One of the best experts on this subject based on the ideXlab platform.
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Countability axioms in i fuzzy topological spaces
Fuzzy Sets and Systems, 2006Co-Authors: Li Qinghua, Fang JinmingAbstract:In this paper, we introduce the concepts of first Countability, second Countability, density, separability, and Lindelof property for I-fuzzy topological spaces, and study their properties and the relationships between them. Furthermore, we give the Lindelof theorem in I-fuzzy topological spaces and describe fuzzy continuous maps by convergence of sequences. Finally, we discuss the productive property of first Countability of I-fuzzy topological spaces.
Francis Bond - One of the best experts on this subject based on the ideXlab platform.
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a plethora of methods for learning english Countability
Empirical Methods in Natural Language Processing, 2003Co-Authors: Timothy Baldwin, Francis BondAbstract:This paper compares a range of methods for classifying words based on linguistic diagnostics, focusing on the task of learning countabilities for English nouns. We propose two basic approaches to feature representation: distribution-based representation, which simply looks at the distribution of features in the corpus data, and agreement-based representation which analyses the level of token-wise agreement between multiple preprocessor systems. We additionally compare a single multiclass classifier architecture with a suite of binary classifiers, and combine analyses from multiple preprocessors. Finally, we present and evaluate a feature selection method.
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learning the Countability of english nouns from corpus data
Meeting of the Association for Computational Linguistics, 2003Co-Authors: Timothy Baldwin, Francis BondAbstract:This paper describes a method for learning the Countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 Countability classes. We were able to assign Countability to English nouns with a precision of 94.6%.
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using an ontology to determine english Countability
International Conference on Computational Linguistics, 2002Co-Authors: Francis Bond, Caitlin VatikiotisbatesonAbstract:In this paper we show to what degree the Countability of English nouns is predictable from their semantics. We found that at 78% of nouns' Countability could be predicted using an ontology of 2,710 nodes. We also show how this predictability can be used to aid non-native speakers to determine the Countability of English nouns when building a bilingual machine translation lexicon.
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Countability and number in japanese to english machine translation
arXiv: Computation and Language, 1995Co-Authors: Francis Bond, Kentaro Ogura, Satoru IkeharaAbstract:This paper presents a heuristic method that uses information in the Japanese text along with knowledge of English Countability and number stored in transfer dictionaries to determine the Countability and number of English noun phrases. Incorporating this method into the machine translation system ALT-J/E, helped to raise the percentage of noun phrases generated with correct use of articles and number from 65% to 73%.
