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Results 1 - 10
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3,897
Simple Morpheme Labelling in Unsupervised Morpheme Analysis
- Advances in Multilingual and Multimodal Information Retrieval, 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007
, 2007
"... Abstract. This paper describes a system for unsupervised morpheme analysis and the results it obtained at Morpho Challenge 2007. The system takes a plain list of words as input and returns a list of labelled morphemic segments for each word. Morphemic segments are obtained by an unsupervised learnin ..."
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Cited by 15 (0 self)
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Abstract. This paper describes a system for unsupervised morpheme analysis and the results it obtained at Morpho Challenge 2007. The system takes a plain list of words as input and returns a list of labelled morphemic segments for each word. Morphemic segments are obtained by an unsupervised
Allomorfessor: Towards unsupervised morpheme analysis
- In Evaluating Systems for Multilingual and Multimodal Information Access – 9th Workshop of the CLEF, Lecture Notes in Computer Science
, 2009
"... Abstract. We extend the unsupervised morpheme segmentation method Morfessor Baseline to account for the linguistic phenomenon of allo-morphy, where one morpheme has several different surface forms. Our method discovers common base forms for allomorphs from an unanno-tated corpus. We evaluate the met ..."
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Cited by 4 (2 self)
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Abstract. We extend the unsupervised morpheme segmentation method Morfessor Baseline to account for the linguistic phenomenon of allo-morphy, where one morpheme has several different surface forms. Our method discovers common base forms for allomorphs from an unanno-tated corpus. We evaluate
MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis
- Working Notes for the CLEF 2009 Workshop Corfu
, 2009
"... This paper investigates a novel approach to unsupervised morphology induction relying on community detection in networks. In a first step, morphological transformation rules are automatically acquired based on graphical similarities between words. These rules encode substring substitutions for trans ..."
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Cited by 9 (0 self)
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This paper investigates a novel approach to unsupervised morphology induction relying on community detection in networks. In a first step, morphological transformation rules are automatically acquired based on graphical similarities between words. These rules encode substring substitutions
Unsupervised morpheme analysis evaluation by IR experiments – Morpho Challenge 2008
- In Working Notes for the CLEF 2008 Workshop
, 2008
"... This paper presents the evaluation and results of Competition 2 (information retrieval experiments) in the Morpho Challenge 2008. Competition 1 (a comparison to linguistic gold standard) is described in a companion paper. In Morpho Challenge 2008 the goal was to search and evaluate unsupervised mach ..."
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Cited by 4 (4 self)
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and the search was based on morphemes instead of words. The results indicate that the morpheme analysis has a significant effect in IR performance in all tested languages (Finnish, English and German). The best unsupervised and language-independent morpheme analysis methods can also rival the best language
Unsupervised morpheme analysis evaluation by a comparison to a linguistic Gold Standard – Morpho Challenge 2007
- In Working Notes for the CLEF 2007 Workshop
, 2007
"... The goal of Morpho Challenge 2008 was to find and evaluate unsupervised algorithms that provide morpheme analyses for words in different languages. Especially in morphologically complex languages, such as Finnish, Turkish and Arabic, morpheme analysis is important for lexical modeling of words in sp ..."
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Cited by 5 (3 self)
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The goal of Morpho Challenge 2008 was to find and evaluate unsupervised algorithms that provide morpheme analyses for words in different languages. Especially in morphologically complex languages, such as Finnish, Turkish and Arabic, morpheme analysis is important for lexical modeling of words
Unsupervised Learning by Probabilistic Latent Semantic Analysis
- Machine Learning
, 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
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Cited by 618 (4 self)
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Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co
Unsupervised Discovery of Morphemes
, 2002
"... We present two methods for unsupervised segmentation of words into morphemelike units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second met ..."
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Cited by 89 (17 self)
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We present two methods for unsupervised segmentation of words into morphemelike units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second
Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines
, 2000
"... We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of ..."
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Cited by 520 (8 self)
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of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression
The "Independent Components" of Natural Scenes are Edge Filters
, 1997
"... It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attem ..."
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Cited by 617 (29 self)
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It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm
Estimating the number of clusters in a dataset via the Gap statistic
, 2000
"... We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. k-means or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference ..."
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Cited by 502 (1 self)
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principal components. 1 Introduction Cluster analysis is an important tool for \unsupervised" learning| the problem of nding groups in data without the help of a response variable. A major challenge in cluster analysis is estimation of the optimal number of \clusters". Figure 1 (top right) shows
Results 1 - 10
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3,897
