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140
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 193 (16 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
ABCD: Eliminating Array Bounds Checks on Demand
, 2000
"... To guarantee execution, Java and other strongly typed languages require bounds checking of array accesses. Because bounds checks may raise exceptions, they block code motion of instructions with side effects, thus preventing many useful code optimizations, such as partial redundancy elimination or i ..."
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Cited by 149 (7 self)
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To guarantee execution, Java and other strongly typed languages require bounds checking of array accesses. Because bounds checks may raise exceptions, they block code motion of instructions with side effects, thus preventing many useful code optimizations, such as partial redundancy elimination or instruction scheduling of memory operations. Furthermore, because it is not expressible at level, the elimination of bounds checks can only be performed at run time, after the program is loaded. Using existing powerful boundscheck optimizers at run time is not feasible, however, because they are too heavyweight for the dynamic compilation setting. ABCD is a lightweight algorithm for elimination of &ray Checks on Demand. Its design emphasizes simplicity and efficiency. In essence, ABCD works by adding a few edges to the SSA value graph and performing a simple traversal of the graph. Despite its simplicity, ABCD is surprisingly powerful. On our benchmarks, ABCD removes on average 45 % of dynamic bound check instructions, sometimes achieving nearideal optimization. The efficiency of ABCD stems from two factors. First, ABCD works on a representation. As a result, it requires on average fewer than 10 simple analysis steps per bounds check. Second, ABCD is demanddriven. It can be applied to a set of frequently executed (hot) bounds checks, which makes it suitable for the dynamiccompilation setting, in which compiletime cost is constrained but hot statements are known.
An Incremental Algorithm for a Generalization of the ShortestPath Problem
, 1992
"... The grammar problem, a generalization of the singlesource shortestpath problem introduced by Knuth, is to compute the minimumcost derivation of a terminal string from each nonterminal of a given contextfree grammar, with the cost of a derivation being suitably defined. This problem also subsume ..."
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Cited by 147 (1 self)
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The grammar problem, a generalization of the singlesource shortestpath problem introduced by Knuth, is to compute the minimumcost derivation of a terminal string from each nonterminal of a given contextfree grammar, with the cost of a derivation being suitably defined. This problem also subsumes the problem of finding optimal hyperpaths in directed hypergraphs (under varying optimization criteria) that has received attention recently. In this paper we present an incremental algorithm for a version of the grammar problem. As a special case of this algorithm we obtain an efficient incremental algorithm for the singlesource shortestpath problem with positive edge lengths. The aspect of our work that distinguishes it from other work on the dynamic shortestpath problem is its ability to handle "multiple heterogeneous modifications": between updates, the input graph is allowed to be restructured by an arbitrary mixture of edge insertions, edge deletions, and edgelength changes.
Learning with hypergraphs: Clustering, classification, and embedding
 Advances in Neural Information Processing Systems (NIPS) 19
, 2006
"... We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many realworld problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevita ..."
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Cited by 75 (2 self)
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We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many realworld problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs instead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classification on the basis of the spectral hypergraph clustering approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs. 1
Shortest Path Algorithms in Transportation Models: Classical and Innovative Aspects
, 1998
"... Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is transportation, where shortest path problems of different kinds need to be solved. Due to the nature of the application, transportation scientists ..."
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Cited by 67 (3 self)
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Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is transportation, where shortest path problems of different kinds need to be solved. Due to the nature of the application, transportation scientists need very flexible and efficient shortest path procedures, both from the running time point of view, and also for the memory requirements. Since no "best" algorithm currently exists for every kind of transportation problem, research in this field has recently moved to the design and implementation of "ad hoc" shortest path procedures, which are able to capture the peculiarities of the problems under consideration. The aim of this work is to present in a unifying framework both the main algorithmic approaches that have been proposed in the past years for solving the shortest path problems arising most frequently in the transportation field, and also some important implementation techniques ...
Optimal Traversal of Directed Hypergraphs
, 1992
"... A directed hypergraph is defined by a set of nodes and a set of hyperarcs, each connecting a set of source nodes to a single target node. Directed hypergraphs are used in several contexts to model different combinatorial structures, such as functional dependencies [Ull82], Horn clauses in proposi ..."
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Cited by 35 (2 self)
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A directed hypergraph is defined by a set of nodes and a set of hyperarcs, each connecting a set of source nodes to a single target node. Directed hypergraphs are used in several contexts to model different combinatorial structures, such as functional dependencies [Ull82], Horn clauses in propositional calculus [AI91], ANDOR graphs [Nil82], Petri nets [Pet62]. A hyperpath, similarly to the notion of path in directed graphs, consists of a connection among nodes using hyperarcs. Unlike paths in graphs, hyperpaths are suitable of different definitions of measure, corresponding to different concepts arising in various applications. In this paper we consider the problem of finding minimal hyperpaths according to several measures. We show that some of these problems are, not surprisingly, NPhard. However, if the measure function on hyperpaths matches certain conditions (which we define as valuebased measure functions) , the problem turns out to be solvable in polynomial time. We...
A unified framework for phrasebased, hierarchical, and syntaxbased statistical machine translation
 In Proceedings of the International Workshop on Spoken Language Translation (IWSLT
, 2009
"... Despite many differences between phrasebased, hierarchical, and syntaxbased translation models, their training and testing pipelines are strikingly similar. Drawing on this fact, we extend the Moses toolkit to implement hierarchical and syntactic models, making it the first open source toolkit wit ..."
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Cited by 31 (4 self)
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Despite many differences between phrasebased, hierarchical, and syntaxbased translation models, their training and testing pipelines are strikingly similar. Drawing on this fact, we extend the Moses toolkit to implement hierarchical and syntactic models, making it the first open source toolkit with endtoend support for all three of these popular models in a single package. This extension substantially lowers the barrier to entry for machine translation research across multiple models. 1.
SCHEDULING WITH AND/OR PRECEDENCE CONSTRAINTS
, 2004
"... In many scheduling applications it is required that the processing of some job be postponed until some other job, which can be chosen from a pregiven set of alternatives, has been completed. The traditional concept of precedence constraints fails to model such restrictions. Therefore, the concept h ..."
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Cited by 30 (1 self)
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In many scheduling applications it is required that the processing of some job be postponed until some other job, which can be chosen from a pregiven set of alternatives, has been completed. The traditional concept of precedence constraints fails to model such restrictions. Therefore, the concept has been generalized to socalled and/or precedence constraints which can cope with this kind of requirement. In the context of traditional precedence constraints, feasibility, transitivity, and the computation of earliest start times for jobs are fundamental, wellstudied problems. The purpose of this paper is to provide efficient algorithms for these tasks for the more general model of and/or precedence constraints. We show that feasibility as well as many questions related to transitivity can be solved by applying essentially the same lineartime algorithm. In order to compute earliest start times we propose two polynomialtime algorithms to cope with different classes of time distances between jobs.
Constructions from dots and lines
 CoRR
"... Summary. A graph is a data structure composed of dots (i.e. vertices) and lines (i.e. edges). The dots and lines of a graph can be organized into intricate arrangements. The ability for a graph to denote objects and their relationships to one another allow for a surprisingly large number of things ..."
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Cited by 20 (1 self)
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Summary. A graph is a data structure composed of dots (i.e. vertices) and lines (i.e. edges). The dots and lines of a graph can be organized into intricate arrangements. The ability for a graph to denote objects and their relationships to one another allow for a surprisingly large number of things to be modeled as a graph. From the dependencies that link software packages to the wood beams that provide the framing to a house, most anything has a corresponding graph representation. However, just because it is possible to represent something as a graph does not necessarily mean that its graph representation will be useful. If a modeler can leverage the plethora of tools and algorithms that store and process graphs, then such a mapping is worthwhile. This article explores the world of graphs in computing and exposes situations in which graphical models are beneficial. 1 The Bits and Pieces of the Dots and Lines A model is a representation of some aspect of reality. Many models can be thought of as a collection of objects (e.g. people, concepts) and the relationships that exist between them (e.g. friendships, subclasses). Such objects and relations form a network. Graphically, an object in a network can be denoted by a dot and a relationship can be denoted by a line. A structure formed by dots and lines is known as a graph—the mathematical term for a network [13]. The most common type of graph is the simple graph. An example instance is diagrammed in Figure 1. In a simple graph there are a set of vertices (i.e. dots) and a set of edges (i.e. lines), where edges are undirected, connect two unique vertices (i.e. no loops), and no two edges exist between the same pair of vertices. Contrary to the title of this article, dots and lines are not the only components in a graph modeler’s toolkit. There are many more bits and pieces