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1
Identifying complaints from product reviews: a case study on Hindi
In: Singh, Raghvendra Pratap, Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2020) Identifying complaints from product reviews: a case study on Hindi. In: 28th Irish Conference on Artificial Intelligence and Cognitive Science, 7-8 Dec 2020, Dublin, Ireland. (2020)
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2
English WordNet Taxonomic Random Walk Pseudo-Corpora
In: Conference papers (2020)
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3
Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
In: Conference papers (2019)
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4
The Observable Representation
In: Entropy ; Volume 21 ; Issue 3 (2019)
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5
Short Text Feature Extraction via Node Semantic Coupling and Graph Structures
In: IFIP Advances in Information and Communication Technology ; 10th International Conference on Intelligent Information Processing (IIP) ; https://hal.inria.fr/hal-02197797 ; 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.173-182, ⟨10.1007/978-3-030-00828-4_18⟩ (2018)
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6
Statistical physics of language evolution : the grammaticalization phenomenon ; Physique statistique de l'évolution des langues : le cas de la grammaticalisation
Feltgen, Quentin. - : HAL CCSD, 2017
In: https://tel.archives-ouvertes.fr/tel-01753835 ; Physics [physics]. Université Paris sciences et lettres, 2017. English. ⟨NNT : 2017PSLEE039⟩ (2017)
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7
PageRank Induced Topology for Real-World Networks
In: https://hal.archives-ouvertes.fr/hal-01322040 ; 2016 (2016)
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8
Comparing and Fusing Terrain Network Information
In: Scalable Uncertainty Management 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012, Proceedings ; Sixth International Conference on Scalable Uncertainty Management (SUM 2012) ; https://hal.archives-ouvertes.fr/hal-00992033 ; Sixth International Conference on Scalable Uncertainty Management (SUM 2012), Sep 2012, Marburg, Germany. pp.459--472, ⟨10.1007/978-3-642-33362-0_35⟩ ; https://link.springer.com/chapter/10.1007%2F978-3-642-33362-0_35 (2012)
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9
Random Walk on WordNet to Measure Lexical Semantic Relatedness
In: http://www.d.umn.edu/~tpederse/Pubs/yanbo-report.pdf (2011)
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10
A Thesaurus-Based Semantic Classification of English Collocations
In: http://wing.comp.nus.edu.sg/~antho/O/O09/O09-5002.pdf (2009)
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11
Generalized additive models : an introduction with R
Wood, Simon N.. - Boca Raton [u.a.] : Chapman & Hall/CRC, 2006
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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12
Query expansion using random walk models
In: http://www.cs.cmu.edu/~callan/Papers/cikm05-kct.pdf (2005)
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13
Media theory
In: http://userwww.sfsu.edu/~sergei/MediaTheory.pdf (2002)
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14
Formalizing Triggers: A Learning Model for Finite Spaces
In: DTIC AND NTIS (1993)
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15
A Thesaurus-Based Semantic Classification of English Collocations
In: http://www.aclclp.org.tw/rocling/2008/OS1_3.pdf
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16
Measuring the Similarity between TV Programs Using Semantic Relations
In: http://aclweb.org/anthology/C/C12/C12-1180.pdf
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17
Extending query translation to cross-language query expansion with markov chain models
In: http://www.iro.umontreal.ca/~nie/Publication/cao-cikm07.pdf
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18
2008. Multi-document summarization using cluster-based link analysis
In: http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2008/2.pdf
Abstract: The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers. Categories and Subject Descriptors:
Keyword: Algorithms; Cluster-based Link Analysis; Conditional Markov Random Walk Model; Content Analysis and Indexing – abstracting methods; Experimentation; H.3.1 [Information Storage and Retrieval; HITS; I.2.7 [Artificial Intelligence; Multi-Document Summarization; Natural Language Processing – text analysis General Terms; Performance Keywords
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.6018
http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2008/2.pdf
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19
Extending query translation to cross-language query expansion with markov chain models
In: http://www.iro.umontreal.ca/~nie/Publication/Cao-CIKM-07.pdf
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20
A Novel Bangla Text Corpus Building Method for Efficient Information Retrieval
In: http://ijcit.org/jcit_papers/vol-1_no-1/JCIT-100708.pdf
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