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A Lexical Database and an Algorithm to Find Words From Definitions
, 2002
"... This paper presents a system to find automatically words from a definition or a paraphrase. The system uses a lexical database of French words that is comparable in its size to WordNet and an algorithm that evaluates distances in the semantic graph between hypernyms and hyponyms of the words in the ..."
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This paper presents a system to find automatically words from a definition or a paraphrase. The system uses a lexical database of French words that is comparable in its size to WordNet and an algorithm that evaluates distances in the semantic graph between hypernyms and hyponyms of the words in the definition. The paper first outlines the structure of the lexical network on which the method is based. It then describes the algorithm. Finally, it concludes with examples of results we have obtained.
Parallel Dynamic Lowest Common Ancestors
 Selected papers of the 4 th Scandinavian Workshop on Algorithm Theory (SWAT '94) ( Arhus
, 1994
"... . This paper gives a CREW PRAM algorithm for the problem of finding lowest common ancestors in a forest under the insertion of leaves and roots and the deletion of leaves. For a forest with a maximum of n vertices, the algorithm takes O(m=p+r log p+min(m; r log n)) time and O(n) space using p proces ..."
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. This paper gives a CREW PRAM algorithm for the problem of finding lowest common ancestors in a forest under the insertion of leaves and roots and the deletion of leaves. For a forest with a maximum of n vertices, the algorithm takes O(m=p+r log p+min(m; r log n)) time and O(n) space using p processors to process a sequence of m operations that are presented over r rounds. Furthermore, lowest common ancestor queries can be done in worst case constant time using a single processor. For one processor, the algorithm matches the bounds achieved by the best sequential algorithm known. The new algorithm is somewhat simpler and has smaller constants in the time and space complexity. 1 Introduction Finding lowest common ancestors in trees is a frequently occurring problem in the literature and has found application in such diverse problems as computing dominators in reducible flow graphs [1], detecting negative cycles in sparse graphs [12], planarity testing [9], and computing weighted matc...
Efficiency of a Good But Not Linear Set Union Algorithm
"... ABSTRACT. TWO types of instructmns for mampulating a family of disjoint sets which partitmn a umverse of n elements are considered FIND(x) computes the name of the (unique) set containing element x UNION(A, B, C) combines sets A and B into a new set named C. A known algorithm for implementing sequen ..."
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ABSTRACT. TWO types of instructmns for mampulating a family of disjoint sets which partitmn a umverse of n elements are considered FIND(x) computes the name of the (unique) set containing element x UNION(A, B, C) combines sets A and B into a new set named C. A known algorithm for implementing sequences of these mstructmns is examined It is shown that, if t(m, n) as the maximum time reqmred by a sequence of m> n FINDs and n 1 intermixed UNIONs, then kima(m, n) _~ t(m, n) < k:ma(m, n) for some positive constants ki and k2, where a(m, n) is related to a functional inverse of Ackermann's functmn and as very slowgrowing.
Nordic Journal of Computing 1(1994), 402–432. PARALLEL DYNAMIC LOWEST COMMON ANCESTORS ∗
"... Abstract. This paper gives a CREW PRAM algorithm for the problem of finding lowest common ancestors in a forest under the insertion of leaves and roots and the deletion of leaves. For a forest with a maximum of n vertices, the algorithm takes O(m/p + r log p + min(m, r log n)) time and O(n) space us ..."
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Abstract. This paper gives a CREW PRAM algorithm for the problem of finding lowest common ancestors in a forest under the insertion of leaves and roots and the deletion of leaves. For a forest with a maximum of n vertices, the algorithm takes O(m/p + r log p + min(m, r log n)) time and O(n) space using p processors to process a sequence of m operations that are presented over r rounds. Furthermore, lowest common ancestor queries can be done in worst case constant time using a single processor. For one processor, the algorithm matches the bounds achieved by the best sequential algorithm known. CR Classification: E.1 Key words: data structures