In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles).
Question: what lexical features did they experiment with?
[EX A]: TF.IDF-based features

[EX Q]: More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. For 13% of the questions, the workers did not agree on one of the 4 categories with a 3 out of 5 majority, so we did not include these questions in our dataset.

The distribution of category labels on the remaining 87% is shown in Table TABREF10 . 14,074 (52%) questions could be answered. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%). This ratio was manually verified based on a random sample of questions.
Question: what dataset statistics are provided?
[EX A]: More than 2,100 texts were paired with 15 questions each, resulting in a total number of approx. 32,000 annotated questions. 13% of the questions are not answerable. Out of the answerable questions, 10,160 could be answered from the text directly (text-based) and 3,914 questions required the use of commonsense knowledge (script-based). The final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).

[EX Q]: Our experiments on a goal-oriented dialog corpus, the personalized bAbI dialog dataset, show that leveraging personal information can significantly improve the performance of dialog systems.
Question: What datasets did they use?
[EX A]:
the personalized bAbI dialog dataset