Sun, 06 Jul 2008 02:04:44 BST CiteULike: vlachmore's library [290 articles] http://www.citeulike.org/user/vlachmore CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/vlachmore/article/2948859 <i>J. Mach. Learn. Res., Vol. 6 (2005), pp. 129-163.</i> Diffusion Kernels on Statistical Manifolds John Lafferty Guy Lebanon J. Mach. Learn. Res., Vol. 6 (2005), pp. 129-163. 2008-07-01T16:53:25-00:00 2005 J. Mach. Learn. Res. 1533-7928 6 129 163 MIT Press heat kernel machines support svms vector http://www.citeulike.org/user/vlachmore/article/519793 <i>(2000)</i><br /><br />This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical models. Our approach approximates full posterior distributions over model parameters and structures, as well as latent variables, in an analytical manner. These posteriors fall out of a free-form optimization procedure, which naturally incorporates conjugate priors. Unlike in large sample approximations, the posteriors are generally nonGaussian and no Hessian needs... A variational Bayesian framework for graphical models H Attias (2000) 2006-02-24T14:33:47-00:00 2000 bayesian graphical models variational_inference http://www.citeulike.org/user/vlachmore/article/531257 <i>(2003), pp. 267-273.</i> Document clustering based on non-negative matrix factorization Wei Xu Xin Liu Yihong Gong doi:10.1145/860435.860485 (2003), pp. 267-273. 2006-03-06T08:47:16-00:00 2003 267 273 ACM Press clustering document factorization matrix non-negative http://www.citeulike.org/user/vlachmore/article/2910900 <i>(2006)</i> Technical Introduction: A Primer on Probabilistic Inference Thomas Griffiths Alan Yuille (2006) 2008-06-20T15:59:07-00:00 2006 inference probabilistic http://www.citeulike.org/user/vlachmore/article/2903283 <i>Neural Comp., Vol. 19, No. 10. (1 October 2007), pp. 2756-2779.</i><br /><br />Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided. Projected Gradient Methods for Nonnegative Matrix Factorization Chih-Jen Lin Neural Comp., Vol. 19, No. 10. (1 October 2007), pp. 2756-2779. 2008-06-17T23:21:40-00:00 2007 Neural Comp. 19 10 2756 2779 factorization matrix non-negative software http://www.citeulike.org/user/vlachmore/article/531251 <i>(2000), pp. 556-562.</i><br /><br />Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary... Algorithms for Non-negative Matrix Factorization Daniel Lee Sebastian Seung (2000), pp. 556-562. 2006-03-06T06:55:34-00:00 2000 556 562 dimensionality factorization matrix non-negative reduction http://www.citeulike.org/user/vlachmore/article/2891300 <i>(2001)</i><br /><br />Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for data that is not real-valued, such as binary-valued data. This paper draws on ideas from the Exponential family, Generalized linear models, and Bregman distances, to give a generalization of PCA to loss functions that we argue are better suited to other data types. We describe algorithms for minimizing the loss... A generalization of principal component analysis to the exponential family M Collins S Dasgupta R Schapire (2001) 2008-06-13T12:58:17-00:00 2001 analysis component dimensionality exponential pca principal reduction http://www.citeulike.org/user/vlachmore/article/2875286 <i>J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577.</i> A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior Hal Iii Daniel Marcu J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577. 2008-06-09T10:49:34-00:00 2005 J. Mach. Learn. Res. 1533-7928 6 1551 1577 MIT Press bayesian clustering dirichlet non-parametric prior process supervised http://www.citeulike.org/user/vlachmore/article/2861694 <i></i><br /><br />Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. Sufficient Dimensionality Reduction Amir Globerson Naftali Tishby 2008-06-04T15:57:24-00:00 dimensionality reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/2861514 <i>Functional Genomics (2003), pp. 159-182.</i><br /><br />Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well. Detecting Stable Clusters Using Principal Component Analysis Asa Ben-Hur Isabelle Guyon doi:10.1385/1-59259-364-X:159 Functional Genomics (2003), pp. 159-182. 2008-06-04T15:42:53-00:00 2003 Functional Genomics 159 182 analysis clustering component dimensionality pca principal reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/2587956 <i>IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4. (2002)</i><br /><br />In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of... Unsupervised Feature Selection Using Feature Similarity P Mitra CA Murthy SK Pal IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4. (2002) 2008-03-26T00:13:20-00:00 2002 IEEE Transactions on Pattern Analysis and Machine Intelligence 24 4 dimensionality reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/510438 <i>Journal of the American Statistical Association, Vol. 101, No. 473. (March 2006), pp. 168-178.</i> Variable Selection for Model-Based Clustering Adrian Raftery Nema Dean doi:10.1198/016214506000000113 Journal of the American Statistical Association, Vol. 101, No. 473. (March 2006), pp. 168-178. 2006-02-18T14:36:37-00:00 2006 Journal of the American Statistical Association 0162-1459 101 473 168 178 American Statistical Association clustering dimensionality reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/2133755 <i>J. Chem. Inf. Comput. Sci., Vol. 40, No. 5. (25 September 2000), pp. 1160-1168.</i><br /><br />Abstract: An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets. Unsupervised Forward Selection: A Method for Eliminating Redundant Variables DC Whitley MG Ford DJ Livingstone doi:10.1021/ci000384c J. Chem. Inf. Comput. Sci., Vol. 40, No. 5. (25 September 2000), pp. 1160-1168. 2007-12-16T21:40:56-00:00 2000 J. Chem. Inf. Comput. Sci. 40 5 1160 1168 clustering dimensionality reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/2861462 <i>(2001)</i><br /><br />We present CLIFF, an algorithm for clustering biological samples using gene expression microarray data. This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high dimensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant. Our algorithm iterates between two computational processes, feature filtering and clustering. Given a reference partition that approximates the correct clustering of the samples, our... CLIFF: Clustering of High-Dimensional Microarray Data via Iterative Feature Filtering Using Normalized Cuts Eric Xing Richard Karp (2001) 2008-06-04T15:22:31-00:00 2001 clustering dimensionality reduction selection unsupervised variable http://www.citeulike.org/user/vlachmore/article/2805713 <i>(2008)</i> Church: a language for generative models Noah Goodman Vikash Mansinghka Daniel Roy Keith Bonawitz Joshua Tenenbaum (2008) 2008-05-16T18:44:37-00:00 2008 church generative models non-parametric http://www.citeulike.org/user/vlachmore/article/1915444 <i>(2005), pp. 363-370.</i> Incorporating non-local information into information extraction systems by Gibbs sampling Jenny Finkel Trond Grenager Christopher Manning doi:10.3115/1219840.1219885 (2005), pp. 363-370. 2007-11-14T19:16:28-00:00 2005 363 370 Association for Computational Linguistics conditional crf fields ner random skip-chain http://www.citeulike.org/user/vlachmore/article/2801724 <i>Journal of Machine Learning Research, Vol. 3 (March 2003), pp. 1157-1182.</i><br /><br />Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods. An Introduction to Variable and Feature Selection Isabelle Guyon André Elisseeff Journal of Machine Learning Research, Vol. 3 (March 2003), pp. 1157-1182. 2008-05-15T14:24:39-00:00 2003 Journal of Machine Learning Research 3 1157 1182 dimensionality feature reduction selection variable http://www.citeulike.org/user/vlachmore/article/2587574 <i>Journal of Machine Learning Research, Vol. 9 (March 2008), pp. 371-421.</i> A Tutorial on Conformal Prediction Glenn Shafer Vladimir Vovk Journal of Machine Learning Research, Vol. 9 (March 2008), pp. 371-421. 2008-03-25T20:09:52-00:00 2008 Journal of Machine Learning Research 9 371 421 conformal learning machine on-line prediction http://www.citeulike.org/user/vlachmore/article/2776008 <i>BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008)</i><br /><br />BACKGROUND:Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate.RESULTS:We present the first comparative evaluation of the diverse PPI corpora, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties. For the evaluation, we unify the corpus PPI annotations to a shared level of information, consisting of undirected, untyped binary interactions of non-static types with no identification of the words specifying the interaction, no negations, and no interaction certainty.We find that the F-score performance of a state-of-the-art PPI extraction method varies on average 19 percentage units and in some cases over 30 percentage units between the different evaluated corpora. The differences stemming from the choice of corpus can thus be substantially larger than differences between the performance of PPI extraction methods, which suggests definite limits on the ability to compare methods evaluated on different resources. We analyse a number of potential sources for these differences and identify factors explaining approximately half of the variance. We further suggest ways in which the difficulty of the PPI extraction tasks codified by different corpora can be determined to advance comparability. Our analysis also identifies points of agreement and disagreement in PPI corpus annotation that are rarely explicitly stated by the authors of the corpora.CONCLUSIONS:Our comparative analysis uncovers key similarities and differences between the diverse PPI corpora, thus taking an important step towards standardization. In the course of this study we have created a major practical contribution in converting the corpora into a shared format. The conversion software is freely available at http://mars.cs.utu.fi/PPICorpora. Comparative analysis of five protein-protein interaction corpora Sampo Pyysalo Antti Airola Juho Heimonen Jari Bjorne Filip Ginter Tapio Salakoski doi:10.1186/1471-2105-9-S3-S6 BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008) 2008-05-09T14:34:10-00:00 2008 BMC Bioinformatics 9 Suppl 3 bionlp interaction protein-protein http://www.citeulike.org/user/vlachmore/article/2670160 <i>BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008)</i><br /><br />BACKGROUND:One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.RESULTS:We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.CONCLUSIONS:The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known. Normalizing biomedical terms by minimizing ambiguity and variability Yoshimasa Tsuruoka John Mcnaught Sophia Ananiadou doi:10.1186/1471-2105-9-S3-S2 BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008) 2008-04-14T17:56:30-00:00 2008 BMC Bioinformatics 9 Suppl 3 bionlp gene normalization rules http://www.citeulike.org/user/vlachmore/article/2775855 <i>Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 652-663.</i><br /><br />There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text. BANNER: an executable survey of advances in biomedical named entity recognition. R Leaman G Gonzalez Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 652-663. 2008-05-09T13:25:09-00:00 2008 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 1793-5091 652 663 bionlp crf entity gene name named ner recognition http://www.citeulike.org/user/vlachmore/article/2765621 <i>(2008)</i> Unsupervised Learning of Narrative Event Chains Nathanael Chambers Dan Jurafsky (2008) 2008-05-07T12:14:02-00:00 2008 learning unsupervised http://www.citeulike.org/user/vlachmore/article/2697015 <i>(2006)</i> Semi-Supervised Learning (2006) 2008-04-21T16:04:15-00:00 2006 MIT Press learning machine semi-supervised http://www.citeulike.org/user/vlachmore/article/1185721 <i>Artif. Intell., Vol. 170, No. 14. (October 2006), pp. 1101-1122.</i> Corrective feedback and persistent learning for information extraction Aron Culotta Trausti Kristjansson Andrew Mccallum Paul Viola doi:10.1016/j.artint.2006.08.001 Artif. Intell., Vol. 170, No. 14. (October 2006), pp. 1101-1122. 2007-03-25T01:13:08-00:00 2006 Artif. Intell. 0004-3702 170 14 1101 1122 Elsevier Science Publishers Ltd. conditional extraction feedback fields information random user http://www.citeulike.org/user/vlachmore/article/2674017 <i>(2003)</i><br /><br />Coreference analysis, also known as record linkage or identity uncertainty, is a difficult and important problem in natural language processing, databases, citation matching and many other tasks. This paper introduces several discriminative, conditionalprobability models for coreference analysis, all examples of undirected graphical models. Unlike many historical approaches to coreference, the models presented here are relational---they do not assume that pairwise coreference decisions ... Toward conditional models of identity uncertainty with application to proper noun coreference A Mccallum B Wellner (2003) 2008-04-15T16:38:49-00:00 2003 conditional coreference fields noun proper random http://www.citeulike.org/user/vlachmore/article/2672890 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.RESULTS:PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article.CONCLUSIONS:We show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully. Natural Language Processing in aid of FlyBase curators Nikiforos Karamanis Ruth Seal Ian Lewin Peter Mcquilton Andreas Vlachos Caroline Gasperin Rachel Drysdale Ted Briscoe doi:10.1186/1471-2105-9-193 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-04-15T11:11:07-00:00 2008 BMC Bioinformatics 9 1 bionlp curation flyslip http://www.citeulike.org/user/vlachmore/article/2658576 <i>(June 2007), pp. 53-56.</i> Semi-Automated Named Entity Annotation Kuzman Ganchev Fernando Pereira Mark Mandel Steven Carroll Peter White (June 2007), pp. 53-56. 2008-04-11T18:23:31-00:00 2007 53 56 Association for Computational Linguistics annotation cost entity experiments mira named recognition timed http://www.citeulike.org/user/vlachmore/article/819622 <i>(1999), pp. 77-86.</i><br /><br />Recently, there has been much effort in making databases for molecular biology more accessible and interoperable. However, information in text form, such as MEDLINE records, remains a greatly underutilized source of biological information. We have begun a research effort aimed at automatically mapping information from text sources into structured representations, such as knowledge bases. Our approach to this task is to use machine-learning methods to induce routines for extracting facts from... Constructing biological knowledge-bases by extracting information from text sources M Craven J Kumlien (1999), pp. 77-86. 2006-08-28T08:52:56-00:00 1999 77 86 bionlp http://www.citeulike.org/user/vlachmore/article/2637717 <i>Computational Linguistics and Intelligent Text Processing (2008), pp. 16-27.</i><br /><br />Previous research has shown that syntactic features are the most informative features in automatic verb classification. We investigate their optimal characteristics by comparing a range of feature sets extracted from data where the proportion of verbal arguments and adjuncts is controlled. The data are obtained from different versions of valex [1] – a large scf lexicon for English which was acquired automatically from several corpora and the Web. We evaluate the feature sets thoroughly using four supervised classifiers and one unsupervised method. The best performing feature set includes rich syntactic information about both arguments and adjuncts of verbs. When combined with our best performing classifier (a novel Gaussian classifier), it yields the promising accuracy of 64.2% in classifying 204 verbs to 17 Levin (1993) classes. We discuss the impact of our results on the state-or-art and propose avenues for future work. Verb Class Discovery from Rich Syntactic Data Lin Sun Anna Korhonen Yuval Krymolowski doi:10.1007/978-3-540-78135-6_2 Computational Linguistics and Intelligent Text Processing (2008), pp. 16-27. 2008-04-07T13:16:29-00:00 2008 Computational Linguistics and Intelligent Text Processing 16 27 classifier clustering frames gaussian semantic subcategorization verb http://www.citeulike.org/user/vlachmore/article/113955 <i>(15 May 1998)</i><br /><br />with a preface by George Miller <P>WordNet, an electronic lexical database, is considered to be the most important resource available to researchers in computational linguistics, text analysis, and many related areas. Its design is inspired by current psycholinguistic and computational theories of human lexical memory. English nouns, verbs, adjectives, and adverbs are organized into synonym sets, each representing one underlying lexicalized concept. Different relations link the synonym sets. <P>The purpose of this volume is twofold. First, it discusses the design of WordNet and the theoretical motivations behind it. Second, it provides a survey of representative applications, including word sense identification, information retrieval, selectional preferences of verbs, and lexical chains. <P>Contributors: Reem Al-Halimi, Robert C. Berwick, J. F. M. Burg, Martin Chodorow, Christiane Fellbaum, Joachim Grabowski, Sanda Harabagiu, Marti A. Hearst, Graeme Hirst, Douglas A. Jones, Rick Kazman, Karen T. Kohl, Shari Landes, Claudia Leacock, George A. Miller, Katherine J. Miller, Dan Moldovan, Naoyuki Nomura, Uta Priss, Philip Resnik, David St-Onge, Randee Tengi, Reind P. van de Riet, Ellen Voorhees. WordNet: An Electronic Lexical Database (Language, Speech, and Communication) (15 May 1998) 2005-03-04T15:24:02-00:00 1998 The MIT Press wordnet http://www.citeulike.org/user/vlachmore/article/1153421 <i>Commun. ACM, Vol. 38, No. 11. (November 1995), pp. 39-41.</i> WordNet: a lexical database for English George Miller doi:10.1145/219717.219748 Commun. ACM, Vol. 38, No. 11. (November 1995), pp. 39-41. 2007-03-11T06:40:46-00:00 1995 Commun. ACM 0001-0782 38 11 39 41 ACM Press wordnet http://www.citeulike.org/user/vlachmore/article/1304139 <i>Journal of the American Statistical Association, Vol. 66 (1971), pp. 622-626.</i> Objective criteria for the evaluation of clustering methods Wwilliam Rand doi:10.2307/2284239 Journal of the American Statistical Association, Vol. 66 (1971), pp. 622-626. 2007-05-17T19:24:25-00:00 1971 Journal of the American Statistical Association 66 622 626 clustering evaluation index rand http://www.citeulike.org/user/vlachmore/article/590638 <i>Advances in Neural Information Processing Systems, Vol. 18 (2006)</i> Interpolating between Types and Tokens by Estimating Power-Law Generators Sharon Goldwater Thomas Griffiths Mark Johnson Advances in Neural Information Processing Systems, Vol. 18 (2006) 2006-04-18T17:41:00-00:00 2006 Advances in Neural Information Processing Systems 18 bayesian non-parametric pitman yor http://www.citeulike.org/user/vlachmore/article/2520315 <i>Language Resources and Evaluation</i><br /><br />Abstract  Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin’s (1993) work which defines groupings of verbs based on syntactic and semantic properties. VerbNet (VN) (Kipper et al. 2000; Kipper-Schuler 2005)—an extensive computational verb lexicon for English—provides detailed syntactic-semantic descriptions of Levin classes. While the classes included are extensive enough for some NLP use, they are not comprehensive. Korhonen and Briscoe (2004) have proposed a significant extension of Levin’s classification which incorporates 57 novel classes for verbs not covered (comprehensively) by Levin. Korhonen and Ryant (unpublished) have recently proposed another extension including 53 additional classes. This article describes the integration of these two extensions into VN. The result is a comprehensive Levin-style classification for English verbs providing over 90% token coverage of the Proposition Bank data (Palmer et al. 2005) and thus can be highly useful for practical applications. A large-scale classification of English verbs Karin Kipper Anna Korhonen Neville Ryant Martha Palmer doi:10.1007/s10579-007-9048-2 Language Resources and Evaluation 2008-03-12T13:47:21-00:00 Language Resources and Evaluation classification semantic verb http://www.citeulike.org/user/vlachmore/article/2515403 <i>(2007)</i> Fast search for Dirichlet process mixture models Hal Daume (2007) 2008-03-11T15:49:06-00:00 2007 dirichlet mixture models multinomials process http://www.citeulike.org/user/vlachmore/article/2515369 <i>(2008)</i> Identification of MCMC Samples for Clustering Kenichi Kurihara Tsuyoshi Murata Taisuke Sato (2008) 2008-03-11T15:42:00-00:00 2008 average clustering mcmc spectral http://www.citeulike.org/user/vlachmore/article/2504970 <i></i><br /><br />We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC) [1] is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data and uses Bayesian hypothesis testing to decide which merges are advantageous and to output the... Randomized Algorithms for Fast Bayesian Hierarchical Clustering Katherine Heller Zoubin Ghahramani 2008-03-11T00:09:42-00:00 bayesian clustering hierarchical http://www.citeulike.org/user/vlachmore/article/1081834 <i>(1994), pp. 300-302.</i><br /><br />We describe the design of Comlex Syntax, a computational lexicon providing detailed syntactic information for approximately 38,000 English headwords. We consider the types of errors which arise in creating such a lexicon, and how such errors can be measured and controlled. 1 Goal The goal of the Comlex Syntax project is to create a moderately-broad-coverage lexicon recording the syntactic features of English words for purposes of computational language analysis. This dictionary is being... The Comlex Syntax Project R Grishman C Macleod S Wolff (1994), pp. 300-302. 2007-02-01T09:48:24-00:00 1994 300 302 comlex lexicon http://www.citeulike.org/user/vlachmore/article/2491840 <i>(1997)</i><br /><br />We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora. Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English. An initial experiment, on a sample of 14 verbs which exhibit multiple complementation patterns, demonstrates that the technique achieves accuracy comparable to previous approaches, which are all limited to a highly restricted set of... Automatic Extraction of Subcategorization from Corpora T Briscoe J Carroll (1997) 2008-03-09T02:14:49-00:00 1997 frame parsing rasp subcategorization http://www.citeulike.org/user/vlachmore/article/2491574 <i>(2000)</i><br /><br />This paper presents the use of probabilistic class-based lexica for disambiguation in target-word selection. Our method employs minimal but precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-based clustering techniques. The method shows promising results in an ... Using a probabilistic class-based lexicon for lexical ambiguity resolution D Prescher S Riezler M Rooth (2000) 2008-03-09T00:47:14-00:00 2000 amibguity disambiguation resolution sense word http://www.citeulike.org/user/vlachmore/article/2491502 <i></i><br /><br />We describe and evaluate the application of a spectral clustering technique (Ng et al., 2002) to the unsupervised clustering of German verbs. Previous work has shown that standard clustering techniques succeed in inducing semantic classes from purely syntactic verb subcategorisation information. But little is known about the behaviour of these techniques when applied to high-dimensional natural language data that probably violate the usual normal assumptions. Spectral clustering performs a... Spectral Clustering for German Verbs Chris Brew Sabine 2008-03-09T00:18:09-00:00 clustering german semantic spectral spectral_clustering verb http://www.citeulike.org/user/vlachmore/article/2491441 <i>(2003)</i><br /><br />Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. In contrast to previous work, we particularly focus on clustering polysemic verbs. A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a... Clustering polysemic subcategorization frame distributions semantically A Korhonen Y Krymolowski Z Marx (2003) 2008-03-08T23:46:47-00:00 2003 clustering frames polysemous semantic subcategorization verb http://www.citeulike.org/user/vlachmore/article/2354608 <i>(22 May 2007)</i><br /><br /><P>The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods.</P> <P>This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory.</P> <P>From the reviews:</P> <P>"...The book is excellent." (Short Book Reviews of the ISI, June 2006)</P> <P>"Now we have All of Nonparametric Statistics . the writing is excellent and the author is to be congratulated on the clarity achieved. the book is excellent." (N.R. Draper, Short Book Reviews, Vol. 26 (1), 2006)</P> <P>"Overall, I enjoyed reading this book very much. I like Wasserman's intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences." (Stergios B. Fotopoulos for Technometrics, Vol. 49, No. 1., February 2007)</P> All of Nonparametric Statistics (Springer Texts in Statistics) Larry Wasserman (22 May 2007) 2008-02-08T19:48:04-00:00 2007 Springer bayesian non-parametric http://www.citeulike.org/user/vlachmore/article/1983303 <i>(2007)</i> A Nonparametric Bayesian Approach to Modeling Overlapping Clusters KA Heller Z Ghahramani (2007) 2007-11-26T03:17:47-00:00 2007 buffet clustering ibp indian mixture model overlap process http://www.citeulike.org/user/vlachmore/article/635668 <i>Advances in Neural Information Processing Systems 17 (2005), pp. 1385-1392.</i><br /><br />We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the “Chinese restaurant franchise.” We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to problems in information retrieval and text modelling. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes Yee Teh Michael Jordan Matthew Beal David Blei Advances in Neural Information Processing Systems 17 (2005), pp. 1385-1392. 2006-05-15T13:16:25-00:00 2005 Advances in Neural Information Processing Systems 17 1385 1392 MIT Press bayesian clustering dirichlet hierarchical process http://www.citeulike.org/user/vlachmore/article/2465524 <i>Bayesian Analysis, Vol. 3, No. 1. (2008), pp. 101-120.</i> How many clusters? Peter Mccullagh J Yang Bayesian Analysis, Vol. 3, No. 1. (2008), pp. 101-120. 2008-03-04T16:33:05-00:00 2008 Bayesian Analysis 3 1 101 120 clustering dirichlet process http://www.citeulike.org/user/vlachmore/article/2449590 <i>(2003)</i> Maximum Likelihood and the Information Bottleneck N Slonim Y Weiss (2003) 2008-02-29T23:20:32-00:00 2003 bottleneck information likelihood maximum http://www.citeulike.org/user/vlachmore/article/2447750 <i>(2008)</i> Multiple Kernel Learning for Speaker Verification Chris Longworth Mark Gales (2008) 2008-02-29T14:02:00-00:00 2008 combination kernel machines speech support svms vector http://www.citeulike.org/user/vlachmore/article/2447739 <i>(2007)</i> Derivative and Parametric Kernels for Speaker Verification Chris Longworth Mark Gales (2007) 2008-02-29T13:56:52-00:00 2007 combination kernels machines speech support svms vector http://www.citeulike.org/user/vlachmore/article/2394375 <i></i><br /><br />In this paper we propose a measure of similarity/association between two partitions of a set of objects. Our motivation is the desire to use the measure to characterize the quality or accuracy of clustering algorithms by somehow comparing the clusters they produce with "ground truth" consisting of classes assigned to the patterns by manual means or some other means in whose veracity there is confidence. Such measures are referred to as "external". Our measure also allows clusterings with... An Information-Theoretic External Cluster-Validity Measure B Dom 2008-02-18T13:45:39-00:00 clustering evaluation