Task: Question Answering
Input (English): Here we introduce the evaluation setup and analyze the results for the article–entity (AEP) placement task. We only report the evaluation metrics for the `relevant' news-entity pairs.  Baselines. We consider the following baselines for this task.

B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 .

B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 . Here we show the evaluation setup for ASP task and discuss the results with a focus on three main aspects, (i) the overall performance across the years, (ii) the entity class specific performance, and (iii) the impact on entity profile expansion by suggesting missing sections to entities based on the pre-computed templates. Baselines. To the best of our knowledge, we are not aware of any comparable approach for this task. Therefore, the baselines we consider are the following:

S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2

S2: Place the news into the most frequent section in INLINEFORM0 
 Question: What baseline model is used?
Output (English): For Article-Entity placement, they consider two baselines: the first one using only salience-based features, and the second baseline checks if the entity appears in the title of the article. 

For Article-Section Placement, they consider two baselines: the first picks the section with the highest lexical similarity to the article, and the second one picks the most frequent section.