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检索条件"机构=Key Lab. of Cloud Computing and Intelligent Technology"

共 223 条记录，以下是81-90 订阅

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Microblog topic detection based on LDA model and single-pass clustering 8th

Microblog topic detection based on LDA model and single-pass...

引用

8th International Conference on Rough Sets and Current Trends in computing, RSCTC 2012

作者： Huang, Bo Yang, Yan Mahmood, Amjad Wang, Hongjun School of Information Science & Technology Southwest Jiaotong University Chengdu610031 China Key Lab of Cloud Computing and Intelligent Technology ChengduSichuan Province610031 China

ISBN: (纸本)9783642321146

Microblogging is a recent social phenomenon of Web2.0 technology, having applications in many domains. It is another form of social media, recognized as Real-Time Web Publishing, which has won an impressive audience acceptance and surprisingly changed online expression and interaction for millions of *** is observed that clustering by topic can be very helpful for the quick retrieval of desired information. We propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. Traditional text mining techniques have no special considerations for short and sparse microblog data. Keeping in view these special characteristics of data, we adopt Single-pass Clustering technique by using Latent Dirichlet Allocation (LDA) Model in place of traditional VSM model, to extract the hidden microblog topics information. Experiments on actual dataset results showed that the proposed method decreased the probabilities of miss and false alarm, as well as reduced the normalized detection cost. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Data mining

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Semi-supervised hierarchical co-clustering

Semi-supervised hierarchical co-clustering

引用

7th International Conference on Rough Sets and Knowledge technology, RSKT 2012

作者： Huang, Feifei Yang, Yan Li, Tao Zhang, Jinyuan Rutayisire, Tonny Mahmood, Amjad School of Information Science and Technology Provincial Key Lab. of Cloud Computing and Intelligent Technology Southwest Jiaotong University Chengdu 610031 China School of Computer Science Florida International University Miami FL 33199 United States

ISBN: (纸本)9783642318993

Hierarchical co-clustering aims at generating dendrograms for the rows and columns of the input data matrix. The limitation of using simple hierarchical co-clustering for document clustering is that it has a lot of feature terms and documents, and it also ignores the semantic relations between feature terms. In this paper a semi-supervised clustering algorithm is proposed for hierarchical co-clustering. In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes. And in the last step, the documents and merged feature terms are clustered using hierarchical co-clustering algorithm. Semantic information is used to measure the similarity during the hierarchical co-clustering process. Experimental results show that the proposed algorithm is effective and efficient. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Xiao, Xinyan Xiong, Deyi Zhang, Min Liu, Qun Lin, Shouxun Key Lab. of Intelligent Info. Processing Chinese Academy of Sciences Institute of Computing Technology China Human Language Technology Institute for Infocomm Research Singapore

ISBN: (纸本)9781937284244

Previous work using topic model for statistical machine translation (SMT) explore topic information at the word level. However, SMT has been advanced from word-based paradigm to phrase/rule-based paradigm. We therefore propose a topic similarity model to exploit topic information at the synchronous rule level for hierarchical phrase-based translation. We associate each synchronous rule with a topic distribution, and select desirable rules according to the similarity of their topic distributions with given documents. We show that our model significantly improves the translation performance over the baseline on NIST Chinese-to-English translation experiments. Our model also achieves a better performance and a faster speed than previous approaches that work at the word level. © 2012 Association for Computational Linguistics.

关键词： Machine translation

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Energy-efficient network routing with discrete cost functions

Energy-efficient network routing with discrete cost function...

引用

9th Annual Conference on Theory and Applications of Models of Computation, TAMC 2012

作者： Wang, Lin Fernández Anta, Antonio Zhang, Fa Hou, Chenying Liu, Zhiyong Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Institute IMDEA Networks Spain China State Key Lab. for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642299513

Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization network routing problem with discrete cost functions. We formulate this problem as an integer program, which is proved to be NP-hard. Then a constant approximation algorithm is developed. In our proposed method, we first transform the program into a continuous-cost network routing problem, and then we approximate the optimal solution by a two-step rounding process. We show by analysis that, for uniform demands, our method provides a constant approximation for the uniform network routing problem with discrete costs. A bicriteria network routing problem is also developed so that a trade-off can be made between energy consumption and network delay. Analytical results for this latter model are also presented. © 2012 Springer-Verlag.

关键词： Network routing

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ICT: System description for CoNLL-2012

ICT: System description for CoNLL-2012

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2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 - Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

作者： Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284459

In this paper, we present our system description for the CoNLL-2012 coreference resolution task on English, Chinese and Arabic. We investigate a projection-based model in which we first translate Chinese and Arabic into English, run a publicly availab.e coreference system, and then use a new projection algorithm to map the coreferring entities back from English into mention candidates detected in the Chinese and Arabic source. We compare to a baseline that just runs the English coreference system on the supplied parses for Chinese and Arabic. Because our method does not beat the baseline system on the development set, we submit outputs generated by the baseline system as our final submission. © 2012 Association for Computational Linguistics.

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ICT: A system combination for Chinese semantic dependency parsing 1

ICT: A system combination for Chinese semantic dependency pa...

引用

1st Joint Conference on Lexical and Computational Semantics, *SEM 2012

作者： Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284220

The goal of semantic dependency parsing is to build dependency structure and lab.l semantic relation between a head and its modifier. To attain this goal, we concentrate on obtaining better dependency structure to predict better semantic relations, and propose a method to combine the results of three state-of-The-Art dependency parsers. Unfortunately, we made a mistake when we generate the final output that results in a lower score of 56.31%in term of lab.led Attachment Score (LAS), reported by organizers. After giving golden testing set, we fix the bug and rerun the evaluation script, this time we obtain the score of 62.8% which is consistentwith the results on developing set. We will report detailed experimental results with correct program as a comparison standard for further research. © 2012 Association for Computational Linguistics.

关键词： Semantics

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ICT: A translation based method for cross-lingual textual entailment 1

ICT: A translation based method for cross-lingual textual en...

引用

1st Joint Conference on Lexical and Computational Semantics, *SEM 2012

作者： Meng, Fandong Xiong, Hao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284220

In this paper, we present our system description in task of Cross-lingual Textual Entailment. The goal of this task is to detect entailment relations between two sentences written in different languages. To accomplish this goal, we first translate sentences written in foreign languages into English. Then, we use EDITS1, an open source package, to recognize entailment relations. Since EDITS only draws monodirectional relations while the task requires bidirectional prediction, thus we exchange the hypothesis and test to detect entailment in another direction. Experimental results show that our method achieves promising results but not perfect results compared to other participants. © 2012 Association for Computational Linguistics.

关键词： Translation (languages)

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Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification

Identifying high-impact sub-structures for convolution kerne...

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Tu, Zhaopeng He, Yifan Foster, Jennifer Van Genabith, Josef Liu, Qun Lin, Shouxun Key Lab. of Intelligent Info. Processing Institute of Computing Technology CAS China Computer Science Department New York University United States School of Computing Dublin City University Ireland

ISBN: (纸本)9781937284251

Convolution kernels support the modeling of complex syntactic information in machinelearning tasks. However, such models are highly sensitive to the type and size of syntactic structure used. It is therefore an important challenge to automatically identify high impact sub-structures relevant to a given task. In this paper we present a systematic study investigating (combinations of) sequence and convolution kernels using different types of substructures in document-level sentiment classification. We show that minimal sub-structures extracted from constituency and dependency trees guided by a polarity lexicon show 1.45 point absolute improvement in accuracy over a bag-of-words classifier on a widely used sentiment corpus. © 2012 Association for Computational Linguistics.

关键词： Convolution

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Head-driven hierarchical phrase-based translation

Head-driven hierarchical phrase-based translation

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Li, Junhui Tu, Zhaopeng Zhou, Guodong Van Genabith, Josef Centre for Next Generation Localisation School of Computing Dublin City University Ireland Key Lab. of Intelligent Info. Processing Chinese Academy of Sciences Institute of Computing Technology China School of Computer Science and Technology Soochow University China

ISBN: (纸本)9781937284251

This paper presents an extension of Chiang's hierarchical phrase-based (HPB) model, called Head-Driven HPB (HD-HPB), which incorporates head information in translation rules to better capture syntax-driven information, as well as improved reordering between any two neighboring non-terminals at any stage of a derivation to explore a larger reordering search space. Experiments on Chinese-English translation on four NIST MT test sets show that the HD-HPB model significantly outperforms Chiang's model with average gains of 1.91 points absolute in BLEU. © 2012 Association for Computational Linguistics.

关键词： Translation (languages)

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Using Syntactic Head Information in Hierarchical Phrase-Based Translation 7

Using Syntactic Head Information in Hierarchical Phrase-Base...

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7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - Human Language Technologies, NAACL HLT 2012

作者： Li, Junhui Tu, Zhaopeng Zhou, Guodong Genabith, Josef Van Centre For Next Generation Localisation School Of Computing Dublin City University Ireland Key Lab. Of Intelligent Info. Processing Institute Of Computing Technology Chinese Academy Of Sciences China School Of Computer Science And Technology Soochow University China

ISBN: (纸本)9781937284206

Chiang's hierarchical phrase-based (HPB) translation model advances the state-of-the-art in statistical machine translation by expanding conventional phrases to hierarchical phrases - phrases that contain sub-phrases. However, the original HPB model is prone to overgeneration due to lack of linguistic knowledge: the grammar may suggest more derivations than appropriate, many of which may lead to ungrammatical translations. On the other hand, limitations of glue grammar rules in the original HPB model may actually prevent systems from considering some reasonable derivations. This paper presents a simple but effective translation model, called the Head-Driven HPB (HD-HPB) model, which incorporates head information in translation rules to better capture syntax-driven information in a derivation. In addition, unlike the original glue rules, the HD-HPB model allows improved reordering between any two neighboring non-terminals to explore a larger reordering search space. An extensive set of experiments on Chinese-English translation on four NIST MT test sets, using both a small and a large training set, show that our HDHPB model consistently and statistically significantly outperforms Chiang's model as well as a source side SAMT-style model. ©2012 Association for Computational Linguistics.

关键词： Syntactics

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