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- W2185053570 abstract "The system utilizes a Recognizing Textual Entailment (RTE) approach. The system uses the question string (q_str) as a Text (T) and one answer (t_str) as a Hypothesis (H). We use two different approach es using machine learning, specifically Support Vector Machine as classifier. The results show an increment ov er the baselines, however enhanced is needed. The features used are unigram, bigram, and trigram overlap of l exemes and stems, Levenshtein distance, tf-idf measure, an d semantic similarity using wordnet. Experimental results show that the best run of our initial system achieved a 0.21 of F-measure and 0.1 7 of QA- accuracy. This shows an increment of 23.53% over the QA accuracy baseline." @default.
- W2185053570 created "2016-06-24" @default.
- W2185053570 creator A5091494488 @default.
- W2185053570 date "2008-01-01" @default.
- W2185053570 modified "2023-10-02" @default.
- W2185053570 title "The Contribution of FaMAF at QA@CLEF 2008.Answer Validation Exercise" @default.
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