By Alt R., Vignes J.
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Extra resources for 10th GAMM-IMACS International Symposium on Scientific Computing, Computer Arithmetic, and Validated Numerics
The distribution of training and testing data is shown in Table 1, where the coarse classes are in capital followed by its ﬁne classes. As listed in the table, some classes consists of a few questions, such as 4 questions in currency and religion class. In experiments, we used two primitive feature types automatically extracted for each question, namely, bag-of-words and bag-of-pos-tags (or pos-tags for short). Question classiﬁcation is a little diﬀerent from text classiﬁcation. Because a question contains a small number of words, while a document can have a large number of words.
For keeping the two words in a semantic sequence, we suggest an improvement of PMI-IR algorithms for our task. We use conjunction operator to construct queries. Because we have limit word to be adjectives in the polarity lexicon. As features are always nouns/noun phrases, so the query of “word feature” would form a normal collocation relation. Scoreconjunction (word, f eature) = log p (word f eature) p (word) p (f eature) (5) Since we calculate the association between w and the feature set Sf eature , we can drop p(word).
42 – 50, 2006.
10th GAMM-IMACS International Symposium on Scientific Computing, Computer Arithmetic, and Validated Numerics by Alt R., Vignes J.