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18
Better kbest parsing
, 2005
"... We discuss the relevance of kbest parsing to recent applications in natural language processing, and develop efficient algorithms for kbest trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s i ..."
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Cited by 185 (17 self)
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We discuss the relevance of kbest parsing to recent applications in natural language processing, and develop efficient algorithms for kbest trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s implementation of Collins ’ lexicalized PCFG model, and on Chiang’s CFGbased decoder for hierarchical phrasebased translation. We show in particular how the improved output of our algorithms has the potential to improve results from parse reranking systems and other applications. 1
Complexity of Axiom Pinpointing in the DLLite Family
"... In real world applications where ontologies are employed, often the knowledge engineer not only wants to know whether her ontology has a certain (unwanted) consequence or not, but also wants to know why it has this consequence. Even for ontologies of moderate size, finding explanations for a given c ..."
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Cited by 8 (1 self)
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In real world applications where ontologies are employed, often the knowledge engineer not only wants to know whether her ontology has a certain (unwanted) consequence or not, but also wants to know why it has this consequence. Even for ontologies of moderate size, finding explanations for a given consequence is
Advanced Dynamic Programming in Semiring and Hypergraph Frameworks
, 2008
"... Dynamic Programming (DP) is an important class of algorithms widely used in many areas of speech and language processing. Recently there have been a series of work trying to formalize many instances of DP algorithms under algebraic and graphtheoretic frameworks. This tutorial surveys two such frame ..."
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Cited by 5 (0 self)
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Dynamic Programming (DP) is an important class of algorithms widely used in many areas of speech and language processing. Recently there have been a series of work trying to formalize many instances of DP algorithms under algebraic and graphtheoretic frameworks. This tutorial surveys two such frameworks, namely semirings and directed hypergraphs, and draws connections between them. We formalize two particular types of DP algorithms under each of these frameworks: the Viterbistyle topological algorithms and the Dijkstrastyle bestfirst algorithms. Wherever relevant, we also discuss typical applications of these algorithms in Natural Language Processing.
Embedding a State Space Model Into a Markov Decision Process
, 2009
"... In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated ..."
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Cited by 2 (1 self)
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In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different ways of discretizing an SSM are discussed and methods for reducing the state space of the MDP are presented. An example from dairy production is given.
Finding the K best policies in finitehorizon Markov decision processes. Submitted
, 2004
"... Directed hypergraphs represent a general modelling and algorithmic tool, which have been successfully used in many different research areas such as artificial intelligence, database systems, fuzzy systems, propositional logic and transportation networks. However, modelling Markov decision processes ..."
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Cited by 2 (2 self)
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Directed hypergraphs represent a general modelling and algorithmic tool, which have been successfully used in many different research areas such as artificial intelligence, database systems, fuzzy systems, propositional logic and transportation networks. However, modelling Markov decision processes using directed hypergraphs has not yet been considered. In this paper we consider finitehorizon Markov decision processes (MDPs) with finite state and action space and present an algorithm for finding the K best policies. That is, we are interested in ranking the first K policies in nondecreasing order using an additive criterion of optimality. The algorithm uses a directed hypergraph to model the finitehorizon MDP. It is shown that the problem of finding the optimal policy can be formulated as a minimum weight hyperpath problem and be solved in linear time, with respect to the input data representing the MDP, using different additive optimality criteria.
nbest parsing revisited
 Proc. of Workshop on ATANLP
, 2010
"... We derive and implement an algorithm similar to (Huang and Chiang, 2005) for finding thenbest derivations in a weighted hypergraph. We prove the correctness and termination of the algorithm and we show experimental results concerning its runtime. Our work is different from the aforementioned one in ..."
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Cited by 2 (2 self)
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We derive and implement an algorithm similar to (Huang and Chiang, 2005) for finding thenbest derivations in a weighted hypergraph. We prove the correctness and termination of the algorithm and we show experimental results concerning its runtime. Our work is different from the aforementioned one in the following respects: we consider labeled hypergraphs, allowing for treebased language models (Maletti and Satta, 2009); we specifically handle the case of cyclic hypergraphs; we admit structured weight domains, allowing for multiple features to be processed; we use the paradigm of functional programming together with lazy evaluation, achieving concise algorithmic descriptions. 1
Faculté des Sciences et Technologies
, 2013
"... pour l’obtention du Diplôme de Doctorat (arrêté du 7 août 2006) et soutenue publiquement le 19 Décembre 2012 par ..."
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pour l’obtention du Diplôme de Doctorat (arrêté du 7 août 2006) et soutenue publiquement le 19 Décembre 2012 par
Finding Best k Policies
"... Abstract. An optimal probabilisticplanning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k best policies problem. The problem is to find the k best policies. The k best policies, k> 1, cannot be found direct ..."
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Abstract. An optimal probabilisticplanning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k best policies problem. The problem is to find the k best policies. The k best policies, k> 1, cannot be found directly using dynamic programming. Näıvely, finding the kth best policy can be Turing reduced to the optimal planning problem, but the number of problems queried in the näıve algorithm is exponential in k. We show empirically that solving k best policy problem by using this reduction requires unreasonable amounts of time even when k = 3. We then provide a new algorithm, based on our theoretical contribution to prove that the kth best policy differs from the ith policy, for some i < k, on exactly one state. We show that the time complexity of the algorithm is quadratic in k, but the number of optimal planning problems it solves is linear in k. We demonstrate empirically that the new algorithm has good scalability. 1