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242
Preliminaries to a Theory of Speech Disfluencies
, 1994
"... This thesis examines disfluencies (e.g., "um", repeated words, and a variety of forms of selfrepair) in the spontaneous speech of adult normal speakers of American English. Despite their prevalence, disfluencies have traditionally been viewed as irregular events and have received little a ..."
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Cited by 174 (7 self)
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This thesis examines disfluencies (e.g., "um", repeated words, and a variety of forms of selfrepair) in the spontaneous speech of adult normal speakers of American English. Despite their prevalence, disfluencies have traditionally been viewed as irregular events and have received little attention. The goal of the thesis is to provide evidence that, on the contrary, disfluencies show remarkably regular trends in a number of dimensions. These regularities have consequences for models of human language production; they can also be exploited to improve performance in speech applications. The method includes analysis of over 5000 handannotated disfluencies from a database (250,000 words) containing three different styles of spontaneous speech: taskoriented humancomputer dialog, taskoriented humanhuman dialog, and humanhuman conversation on a prescribed topic. The approach is theoryneutral and strongly datadriven. The annotations correspond to observable characteristics ("features") ...
Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models
, 1993
"... Ways of obtaining approximate Bayes factors for generalized linear models are described, based on the Laplace method for integrals. I propose a new approximation which uses only the output of standard computer programs such as GUM; this appears to be quite accurate. A reference set of proper priors ..."
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Cited by 148 (28 self)
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Ways of obtaining approximate Bayes factors for generalized linear models are described, based on the Laplace method for integrals. I propose a new approximation which uses only the output of standard computer programs such as GUM; this appears to be quite accurate. A reference set of proper priors is suggested, both to represent the situation where there is not much prior information, and to assess the sensitivity of the results to the prior distribution. The methods can be used when the dispersion parameter is unknown, when there is overdispersion, to compare link functions, and to compare error distributions and variance functions. The methods can be used to implement the Bayesian approach to accounting for model uncertainty. I describe an application to inference about relative risks in the presence of control factors where model uncertainty is large and important. Software to implement the
Knowledge discovery and interestingness measures: A survey
, 1999
"... Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analy ..."
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Cited by 60 (1 self)
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Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. An important problem in the area of data mining is the development of effective measures of interestingness for ranking the discovered knowledge. In this report, we provide a general overview of the more successful and widely known data mining techniques and algorithms, and survey seventeen interestingness measures from the literature that have been successfully employed in data mining applications. 1 1
Probabilistic Syntax
, 2002
"... istic methods for syntax, just as for a long time McCarthy and Hayes (1969) discouraged exploration of probabilistic methods in Artificial Intelligence. Among his arguments were that: (i) Probabilistic models wrongly mix in world knowledge (New York occurs more in text than Dayton, Ohio, but for no ..."
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Cited by 53 (1 self)
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istic methods for syntax, just as for a long time McCarthy and Hayes (1969) discouraged exploration of probabilistic methods in Artificial Intelligence. Among his arguments were that: (i) Probabilistic models wrongly mix in world knowledge (New York occurs more in text than Dayton, Ohio, but for no linguistic reason), (ii) Probabilistic models don't model grammaticality (neither Colorless green ideas sleep furiously nor Furiously sleep ideas green colorless have previously been uttered  and hence must be estimated to have probability zero, Chomsky wrongly assumes  but the former is grammatical while the latter is not, and (iii) Use of probabilities does not meet the goal of describing the mindinternal Ilanguage as opposed to the observedintheworld Elanguage. This chapter is not meant to be a detailed critique of Chomsky's arguments  Abney (1996) provides a survey and a rebuttal, and Pereira (2000) has further useful discussion  but some of these concerns are still importa
Switching Regression Models with Imperfect Sample Separation Information  with an Application to Cartel Stability
, 1982
"... An exogenous switching regression model with imperfect regime classification information is specified and applied to a study of cartel stability. An efficient estimation method is proposed which takes ~his imperfect information into account. The consequences of misclassification are analyzed.' ..."
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Cited by 53 (1 self)
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An exogenous switching regression model with imperfect regime classification information is specified and applied to a study of cartel stability. An efficient estimation method is proposed which takes ~his imperfect information into account. The consequences of misclassification are analyzed.' The direction of the least squares bias is derived. An optimal regime classification rule is obtained and compared theoretically and empirically with other classification rules. We then examine the Joint Executive Committee, a railroad cartel in the l880s. The econometric evidence indicates that reversions to noncooperative behavior did occur for the firms in our sample, and these reversions involve a significant decrease in market price.
Higher Lawrence configurations
 J. Combin. Theory Ser. A
"... Abstract. Any configuration of lattice vectors gives rise to a hierarchy of higherdimensional configurations which generalize the Lawrence construction in geometric combinatorics. We prove finiteness results for the Markov bases, Graver bases and facet posets of these configurations, and we discuss ..."
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Cited by 45 (2 self)
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Abstract. Any configuration of lattice vectors gives rise to a hierarchy of higherdimensional configurations which generalize the Lawrence construction in geometric combinatorics. We prove finiteness results for the Markov bases, Graver bases and facet posets of these configurations, and we discuss applications to the statistical theory of loglinear models. 1.
A finiteness theorem for markov bases of hierarchical models
 J. COMB. THEORY SER. A
, 2007
"... We show that the complexity of the Markov bases of multidimensional tables stabilizes eventually if a single table dimension is allowed to vary. In particular, if this table dimension is greater than a computable bound, the Markov bases consist of elements from Markov bases of smaller tables. We giv ..."
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Cited by 39 (4 self)
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We show that the complexity of the Markov bases of multidimensional tables stabilizes eventually if a single table dimension is allowed to vary. In particular, if this table dimension is greater than a computable bound, the Markov bases consist of elements from Markov bases of smaller tables. We give an explicit formula for this bound in terms of Graver bases. We also compute these Markov and Graver complexities for all K × 2 × 2 × 2 tables.
Interruptions in group discussions: the effects of gender and group composition
 American Sociological Review
, 1989
"... ..."