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• W2234386496 abstract "paper presents our work in the Multilingual Opinion Analysis Task (MOAT) done during the NTCIR-8 evaluation campaign. We suggested a probabilistic model derived from Muller's method (1) that allows us to determine and weight terms (isolated words, bigram of words, noun phrases, etc.) belonging to a given category (or subset of the corpus) compared to the rest of the corpus. Based on these terms and their weights, we have adopted the logistic regression method in order to define the most probable category for each input sentence. Our participation was strongly motivated by the objective to suggest an approach on the polarity subtask of the MOAT with a minimal linguistic component with a possibility to have its performance improved by natural language specific tools. Thus, for the English language, we have adopted a combination of both machine learning approach (Z score and logistic regression) and a polarity dictionary (linguistic component). For the traditional Chinese and Japanese languages however, our current system is limited to a machine learning scheme." @default.
• W2234386496 created "2016-06-24" @default.
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• W2234386496 date "2010-01-01" @default.
• W2234386496 modified "2023-09-26" @default.
• W2234386496 title "Opinion Detection by Combining Machine Learning & Linguistic Tools" @default.
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