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Discriminative Word Alignment via Alignment Matrix Modeling
"... In this paper a new discriminative word alignment method is presented. This approach models directly the alignment matrix by a conditional random field (CRF) and so no restrictions to the alignments have to be made. Furthermore, it is easy to add features and so all available information can be used ..."
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In this paper a new discriminative word alignment method is presented. This approach models directly the alignment matrix by a conditional random field (CRF) and so no restrictions to the alignments have to be made. Furthermore, it is easy to add features and so all available information can be used. Since the structure of the CRFs can get complex, the inference can only be done approximately and the standard algorithms had to be adapted. In addition, different methods to train the model have been developed. Using this approach the alignment quality could be improved by up to 23 percent for 3 different language pairs compared to a combination of both IBM4alignments. Furthermore the word alignment was used to generate new phrase tables. These could improve the translation quality significantly. 1
IPAL at ImageClef 2007 Mixing Features, Models and Knowledge.
"... This paper presents IPAL adhoc photographic retrieval and medical image retrieval results in the ImageClef 2007 campaign. For the photo task, IPAL group is ranked at the 3rd place among 20 participants. The MAP of our best run is 0.2833, which is ranked at the 6th place among the 476 runs. The IPAL ..."
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This paper presents IPAL adhoc photographic retrieval and medical image retrieval results in the ImageClef 2007 campaign. For the photo task, IPAL group is ranked at the 3rd place among 20 participants. The MAP of our best run is 0.2833, which is ranked at the 6th place among the 476 runs. The IPAL system is based on the mixed modality search, i.e. textual and visual modalities. Compare with our results in 2006, our results are significantly enhanced by extracting multiple lowlevel visual content descriptors and fusing multiple CBIR. Several text based image search (TBIR) engines are also developed such as the language model (LM) approach, the latent semantic indexing (LSI) approach. We also have used external knowledge like Wordnet, and Wikipedia for document expansion. Then the crossmodality pseudorelevance feedback is applied to boost each individual modality. Linear fusion is used to combine different ranking lists. Combining the CBIR and TBIR outperforms the individual modality search. On medical side, our run ranks 19 among 111. We continue to use a conceptual indexing with vector space weighting, but we add this year a Bayesian network on concepts extracted from UMLS metathesaurus.
AN EFFICIENT TEXT CLUSTERING ALGORITHM USING AFFINITY PROPAGATION
"... The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and exper ..."
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The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participants preferences for the parameters of simulations.This paper proposes an efficient Affinity Propagation algorithm that guaranteesthe same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters. The problem of clustering has been studied widely in the databaseand statistics literature in the context of a wide variety of data mining tasks. The clustering problem is defined to be that of findinggroups of similar objects in the data.The similarity between the objects is measured with the use of a similarity function. The problemof clustering can be very useful in the text domain, where the objectsto be clusters can be of different granularities
A Maximum Expected Utility Framework for Binary Sequence Labeling
"... We consider the problem of predictive inference for probabilistic binary sequence labeling models under Fscore as utility. For a simple class of models, we show that the number of hypotheses whose expected Fscore needs to be evaluated is linear in the sequence length and present a framework for eff ..."
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We consider the problem of predictive inference for probabilistic binary sequence labeling models under Fscore as utility. For a simple class of models, we show that the number of hypotheses whose expected Fscore needs to be evaluated is linear in the sequence length and present a framework for efficiently evaluating the expectation of many common loss/utility functions, including the Fscore. This framework includes both exact and faster inexact calculation methods.
A Maximum Expected Utility Framework for Binary Sequence Labeling
"... We consider the problem of predictive inference for probabilistic binary sequence labeling models under Fscore as utility. For a simple class of models, we show that the number of hypotheses whose expected Fscore needs to be evaluated is linear in the sequence length and present a framework for eff ..."
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We consider the problem of predictive inference for probabilistic binary sequence labeling models under Fscore as utility. For a simple class of models, we show that the number of hypotheses whose expected Fscore needs to be evaluated is linear in the sequence length and present a framework for efficiently evaluating the expectation of many common loss/utility functions, including the Fscore. This framework includes both exact and faster inexact calculation methods.