Teacher: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
Teacher: Now, understand the problem? If you are still confused, see the following example:
Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Solution: es
Reason: This review is written in spanish. Hence, the label is 'es'.

Now, solve this instance: This paper presents a new dataset with annotations of products coming from online cybercrime forums. The paper is clear and well-written and the experiments are good. Every hypothesis is tested and compared to each other. However, I do have some concerns about the paper: 1. The authors took the liberty to change the font size and the line spacing of the abstract, enabling them to have a longer abstract and to fit the content into the 8 pages requirement. 2. I don't think this paper fits the tagging, chunking, parsing area, as it is more an information extraction problem. 3. I have difficulties to see why some annotations such as sombody in Fig. 1 are related to a product. 4. The basic results are very basic indeed and - with all the tools available nowadays in NLP -, I am sure that it would have been possible to have more elaborate baselines without too much extra work. 5. Domain adaptation experiments corroborate what we already know about user-generated data where two forums on video games, e.g., may have different types of users (age, gender, etc.) leading to very different texts. So this does not give new highlights on this specific problem.
Student:
en