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Building and evaluating resources for sentiment analysis in the Greek language
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Building and evaluating resources for sentiment analysis in the Greek language
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Cognitive agents and machine learning by example : representation with conceptual graphs
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What’s new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
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What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
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Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia
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Towards detection of influential sentences affecting reputation in Wikipedia
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Abstract:
Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in Wikipedia articles have direct and obvious impact on people's opinions towards the mentioned named entities. This paper defines and tackles the problem of reputation-influential sentence detection in Wikipedia articles from various domains. We leverage multiple lexicons, to generate domain independent features. We generate topical features and word embedding features from unlabelled dataset, to boost the classification performance. We conduct several experiments, to prove the effectiveness of these features. We further adapt a two-step binary classification method, to perform multi-classification. Our evaluation results show that this method outperforms the state-of-the-art one-vs-one multi-classification method for this problem.
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Keyword:
ZA4050 Electronic information resources
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URL: http://wrap.warwick.ac.uk/78605/ http://wrap.warwick.ac.uk/78605/1/WRAP_WikiSentenceCLS-1.pdf https://doi.org/10.1145/2908131.2908177
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Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia
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Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications
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Combining heterogeneous user generated data to sense well-being
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Analysing entity context in multilingual wikipedia to support entity-centric retrieval applications ...
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WarwickDCS : from phrase-based to target-specific sentiment recognition
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Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop
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