Results 1  10
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20
Learning compatibility coefficients for relaxation labeling processes
 IEEE Trans. Pattern Anal. Machine Intell
, 1994
"... AbstractRelaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation o ..."
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Cited by 44 (5 self)
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AbstractRelaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of “compatibility coefficients. ” The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statisticalbased methods have been suggested. In this paper, we propose a rather different viewpoint to solve this problem: we derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach. Index Terms Compatibility coefficients, constraint satisfaction, gradient projection, learning, neural networks, nonlinear
Mapping WordNets Using Structural Information
 IN PROCEEDINGS 38 TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS(ACL00). HONG KONG
, 2000
"... We present a robust approach for linking already existing lexi cal/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select among a set of candidates the node in a target taxonomy that bests matches each node in a source tax onomy. In particular, we ..."
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Cited by 38 (9 self)
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We present a robust approach for linking already existing lexi cal/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select among a set of candidates the node in a target taxonomy that bests matches each node in a source tax onomy. In particular, we use it to map the nominal part of WordNet 1.5 onto WordNet 1.6, with a very high precision and a very low remaining ambiguity.
The Dynamics of Nonlinear Relaxation Labeling Processes
, 1997
"... We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symm ..."
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Cited by 37 (11 self)
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We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symmetry condition is met, the algorithm possesses a Liapunov function which turns out to be (the negative of) a wellknown consistency measure. This follows almost immediately from a powerful result of Baum and Eagon developed in the context of Markov chain theory. Moreover, it is seen that most of the essential dynamical properties of the algorithm are retained when the symmetry restriction is relaxed. These properties are also shown to naturally generalize to higherorder relaxation schemes. Some applications and implications of the presented results are finally outlined.
A Flexible POS Tagger Using an Automatically Acquired Language Model
"... We present an algorithm that automatically learns context constraints using sta tistical decision trees. We then use the ac quired constraints in a flexible POS tagger. The tagger is able to use information of any degree: ngrams, automatically learned context constraints, linguistically motivated ..."
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Cited by 17 (9 self)
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We present an algorithm that automatically learns context constraints using sta tistical decision trees. We then use the ac quired constraints in a flexible POS tagger. The tagger is able to use information of any degree: ngrams, automatically learned context constraints, linguistically motivated manually written con straints, etc. The sources and kinds of con straints are unrestricted, and the language model can be easily extended, improving the results. The tagger has been tested and evaluated on the WSJ corpus.
Developing a hybrid NP parser
 In Proceedings of ANLP97
, 1997
"... We describe the use of energy function optimization in very shallow syntactic parsing. The approach can use linguistic rules and corpusbased statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints ..."
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Cited by 12 (4 self)
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We describe the use of energy function optimization in very shallow syntactic parsing. The approach can use linguistic rules and corpusbased statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpusbased ngrams of syntactic tags. The success of the hybrid syntactic disambiguator is evaluated against a heldout benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated. 1
POS Tagging Using Relaxation Labelling
 PROCEEDINGS OF 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, COLING
, 1996
"... Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies  to the maximum possible degree  a set of given constraints. This pat)er scribes some experiment ..."
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Cited by 12 (5 self)
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Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies  to the maximum possible degree  a set of given constraints. This pat)er scribes some experiments performed applying it to POS tagging, and the results obtained. it also ponders the possibility of applying it, to Word Sense Disambiguation.
A Complete wn1.5 to wn1.6 Mapping
"... We describe a robust approach for linking already existing lexical/semantic hierarchies. We use a constraint satisfaction algorithm (relaxation labelling) to select among a set of candidates the node in a target taxonomy that bests matches each node in a source taxonomy. In this paper we present ..."
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Cited by 9 (5 self)
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We describe a robust approach for linking already existing lexical/semantic hierarchies. We use a constraint satisfaction algorithm (relaxation labelling) to select among a set of candidates the node in a target taxonomy that bests matches each node in a source taxonomy. In this paper we present the complete mapping of the nominal, verbal, adjectival and adverbial parts of WordNet 1.5 onto WordNet 1.6.
Constraint satisfaction as global optimization
 In Proceedings of the 14th IJCAI
, 1995
"... We present a optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a ..."
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Cited by 8 (1 self)
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We present a optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a partial solution violating a minimal number of constraints, if the problem is unsolvable. This approach is based on relaxation labeling techniques used to enforce consistency in image interpretation. We have used a projected gradient ascent algorithm to maximize A(P) on the nqueens problem obtaining good results but with a high computational cost. To elude this problem, we have developed a heuristic for variable and value selection inspired in the direction in which A(P) is maximized. We have tested this heuristic with forward checking on several classes of CSP. 1
Making Wordnet Mappings Robust
, 2003
"... Building appropriate resources for broadcoverage semantic processing is a hard and expensive task, involving large research groups during long periods of developement. The outcomes of these projects are, usually, large and complex semantic structures, not compatible with resources developed in ..."
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Cited by 7 (2 self)
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Building appropriate resources for broadcoverage semantic processing is a hard and expensive task, involving large research groups during long periods of developement. The outcomes of these projects are, usually, large and complex semantic structures, not compatible with resources developed in previous projects and efforts. To maintain compatibility between wordnets of different languages and versions, past and new, it is fundamental to dispose of a high accurate tool. In this
Mapping Multilingual Hierarchies Using Relaxation Labeling
 IN JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA (EMNLP/VLC'99
, 1999
"... This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to selectamo ..."
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Cited by 7 (3 self)
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This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to selectamong all the candidate translations proposed by a bilingual dictionary the right English WordNet synset for each sense in a. taxonomy automatically derived from a Spanish monolingual dictionary. Although on average, there are 15 possible WordNet connections for each sense in the taxonomy, the method achieves an accuracy over 80%. Finally, we also propose several ways in which this technique could be applied to enrich and improve existing lexical databases.