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1
Building and evaluating resources for sentiment analysis in the Greek language [<Journal>]
Tsakalidis, Adam [Verfasser]; Papadopoulos, Symeon [Sonstige]; Voskaki, Rania [Sonstige].
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2
Building and evaluating resources for sentiment analysis in the Greek language
Tsakalidis, Adam; Papadopoulos, Symeon; Voskaki, Rania. - : Springer Netherlands, 2018
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3
Building and evaluating resources for sentiment analysis in the Greek language
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4
Cognitive agents and machine learning by example : representation with conceptual graphs
Gkiokas, Alexandros; Cristea, Alexandra I.. - : Wiley-Blackwell Publishing Ltd., 2018
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5
What’s new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
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6
What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
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7
A review on corpus annotation for arabic sentiment analysis
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8
Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia
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9
Towards detection of influential sentences affecting reputation in Wikipedia
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10
Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia
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11
Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications
Zhou, Yiwei; Demidova, Elena; Cristea, Alexandra I.. - : Springer International Publishing, 2016
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12
Combining heterogeneous user generated data to sense well-being
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13
Analysing entity context in multilingual wikipedia to support entity-centric retrieval applications
Zhou, Yiwei [Verfasser]; Demidova, Elena [Verfasser]; Cristea, Alexandra I. [Verfasser]. - Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2015
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14
Analysing entity context in multilingual wikipedia to support entity-centric retrieval applications ...
Zhou, Yiwei; Demidova, Elena; Cristea, Alexandra I.. - : Heidelberg : Springer Verlag, 2015
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15
WarwickDCS : from phrase-based to target-specific sentiment recognition
Abstract: We present and evaluate several hybrid systems for sentiment identification for Twitter, both at the phrase and document (tweet) level. Our approach has been to use a novel combination of lexica, traditional NLP and deep learning features. We also analyse techniques based on syntactic parsing and tokenbased association to handle topic specific sentiment in subtask C. Our strategy has been to identify subphrases relevant to the designated topic/target and assign sentiment according to our subtask A classifier. Our submitted subtask A classifier ranked fourth in the SemEval official results while our BASELINE and µPARSE classifiers for subtask C would have ranked second.
Keyword: HM Sociology; QA76 Electronic computers. Computer science. Computer software
URL: http://wrap.warwick.ac.uk/71340/
http://wrap.warwick.ac.uk/71340/7/WRAP_Townsend_WarwickDCS_s15-2110.pdf
https://doi.org/10.18653/v1/S15-2110
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16
Phrase-Level Word Embeddings
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17
Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop
Cristea, Alexandra I.; Hendrix, M.; Nejdl, W.. - : Springer Berlin Heidelberg, 2006
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