Results 1  10
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48
Data Mining of User Navigation Patterns
, 2000
"... We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as ahypertext probabilistic grammar whose higher probability strings correspond to the user's preferred trails. An algorithm to efficiently mine suchtrailsisgiven. Wemak ..."
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Cited by 113 (18 self)
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We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as ahypertext probabilistic grammar whose higher probability strings correspond to the user's preferred trails. An algorithm to efficiently mine suchtrailsisgiven. Wemake use of the Ngram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar's statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar's entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.
Parameter learning of logic programs for symbolicstatistical modeling
 Journal of Artificial Intelligence Research
, 2001
"... We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distributio ..."
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Cited by 91 (19 self)
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We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, thatrunsfora class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the BaumWelch algorithm for HMMs, the InsideOutside algorithm for PCFGs, and the one for singly connected Bayesian networks that have beendeveloped independently in each research eld. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can signi cantly outperform the InsideOutside algorithm. 1.
Generalized queries on probabilistic contextfree grammars
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... Abstract—Probabilistic contextfree grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a contextfree language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or ..."
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Cited by 33 (2 self)
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Abstract—Probabilistic contextfree grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a contextfree language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed and applied to a variety of patternrecognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamicprogramming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure. Index Terms—Probabilistic contextfree grammars, Bayesian networks.
A sensory grammar for inferring behaviors in sensor networks
 In Proceedings of Information Processing in Sensor Networks (IPSN
, 2006
"... The ability of a sensor network to parse out observable activities into a set of distinguishable actions is a powerful feature that can potentially enable many applications of sensor networks to everyday life situations. In this paper we introduce a framework that uses a hierarchy of Probabilistic C ..."
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Cited by 32 (17 self)
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The ability of a sensor network to parse out observable activities into a set of distinguishable actions is a powerful feature that can potentially enable many applications of sensor networks to everyday life situations. In this paper we introduce a framework that uses a hierarchy of Probabilistic Context Free Grammars (PCFGs) to perform such parsing. The power of the framework comes from the hierarchical organization of grammars that allows the use of simple local sensor measurements for reasoning about more macroscopic behaviors. Our presentation describes how to use a set of phonemes to construct grammars and how to achieve distributed operation using a messaging model. The proposed framework is flexible. It can be mapped to a network hierarchy or can be applied sequentially and across the network to infer behaviors as they unfold in space and time. We demonstrate this functionality by inferring simple motion patterns using a sequence of simple direction vectors obtained from our camera sensor network testbed.
Memorybased models of melodic analysis: Challenging the gestalt principles
 Journal of New Music Research
, 2002
"... We argue for a memorybased approach to music analysis which works with concrete musical experiences rather than with abstract rules or principles. New pieces of music are analyzed by combining fragments from structures of previously encountered pieces. The occurrencefrequencies of the fragments ar ..."
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Cited by 31 (5 self)
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We argue for a memorybased approach to music analysis which works with concrete musical experiences rather than with abstract rules or principles. New pieces of music are analyzed by combining fragments from structures of previously encountered pieces. The occurrencefrequencies of the fragments are used to determine the preferred analysis of a piece. We test some instances of this approach against a set of 1,000 manually annotated folksongs from the Essen Folksong Collection, yielding up to 85.9 % phrase accuracy. A qualitative analysis of our results indicates that there are grouping phenomena that challenge the commonly accepted Gestalt principles of proximity, similarity and parallelism. These grouping phenomena can neither be explained by other musical factors, such as meter and harmony. We argue that music perception may be much more memorybased than previously assumed. 1.
Novel Estimation Methods for Unsupervised Discovery of Latent Structure in Natural Language Text
, 2006
"... This thesis is about estimating probabilistic models to uncover useful hidden structure in data; specifically, we address the problem of discovering syntactic structure in natural language text. We present three new parameter estimation techniques that generalize the standard approach, maximum likel ..."
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Cited by 28 (8 self)
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This thesis is about estimating probabilistic models to uncover useful hidden structure in data; specifically, we address the problem of discovering syntactic structure in natural language text. We present three new parameter estimation techniques that generalize the standard approach, maximum likelihood estimation, in different ways. Contrastive estimation maximizes the conditional probability of the observed data given a “neighborhood” of implicit negative examples. Skewed deterministic annealing locally maximizes likelihood using a cautious parameter search strategy that starts with an easier optimization problem than likelihood, and iteratively moves to harder problems, culminating in likelihood. Structural annealing is similar, but starts with a heavy bias toward simple syntactic structures and gradually relaxes the bias. Our estimation methods do not make use of annotated examples. We consider their performance in both an unsupervised model selection setting, where models trained under different initialization and regularization settings are compared by evaluating the training objective on a small set of unseen, unannotated development data, and supervised model selection, where the most accurate model on the development set (now with annotations)
Some computational complexity results for synchronous contextfree grammars
 In Proceedings of HLT/EMNLP05
, 2005
"... This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation. These models can be viewed as pairs of probabilistic contextfree grammars working in a ‘synchronous’ way. Two hardness result ..."
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Cited by 23 (3 self)
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This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation. These models can be viewed as pairs of probabilistic contextfree grammars working in a ‘synchronous’ way. Two hardness results for the class NP are reported, along with an exponential time lowerbound for certain classes of algorithms that are currently used in the literature. 1
A Fine Grained Heuristic to Capture Web Navigation Patterns
 SIGKDD Explorations
, 2000
"... In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information, obtained from web logs, is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest p ..."
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Cited by 21 (7 self)
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In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information, obtained from web logs, is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a BreadthFirst Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cutpoint, which we call the rules. Although the algorithm's running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cutpoint is small and a small set of very short rules when the cutpoint is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first ex...
Consistency of Stochastic ContextFree Grammars from Probabilistic Estimation based on Growth Transformations
, 1997
"... An important problem related to the probabilistic estimation of Stochastic ContextFree Grammars (SCFGs) is guaranteeing the consistency of the estimated model. ..."
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Cited by 20 (1 self)
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An important problem related to the probabilistic estimation of Stochastic ContextFree Grammars (SCFGs) is guaranteeing the consistency of the estimated model.