instruction:
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].
question:
__Note__: An earlier version of the review (almost identical to the present one) for an earlier version of the paper (available on arXiV) can be found here:
answer:
en


question:
De forma: •	Se recomienda cambiar la palabra “catastros” del título •	Problemas de ortografía. Ejemplo: agiles (primer párrafo de la introducción), ingles (sección criterios de inclusión y exclusión) •	Se recomienda siempre redactar en tercera persona para textos en español. •	Las páginas 6 y 8 del artículo están en blanco.  De fondo: •	El artículo es claro en su estructura y de fácil lectura. •	Se muestra de forma inicial el proceso seguido para el mapeo sistemático y luego de forma ordenada se muestran los resultados al ejecutar cada paso. •	Se recomienda cambiar la figura 2 al idioma español, para los casos en que sea posible. •	Se aborda un tema interesante y relevante para la comunidad científica y empresarial, y el alcance logrado está acorde con el tipo de productos solicitados para el evento INFONOR.
answer:
es


question:
The aim of this paper is to show that distributional information stored in word vector models contain information about POS labels. They use a version of the BNC annotated with UD POS and in which words have been replaced by lemmas. They train word embeddings on this corpus, then use the resulting vectors to train a logistic classifier to predict the word POS. Evaluations are performed on the same corpus (using cross-validation) as well as on other corpora. Results are clearly presented and discussed and analyzed at length.  The paper is clear and well-written. The main issue with this paper is that it does not contain anything new in terms of NLP or ML. It describe a set of straightforward experiments without any new NLP or ML ideas or methods. Results are interesting indeed, in so far that they provide an empirical grounding to the notion of POS. In that regard, it is certainly worth being published in a (quantitative/emprirical) linguistic venue.  On another note, the literature on POS tagging and POS induction using word embeddings should be cited more extensively (cf. for instance Lin, Ammar, Duer and Levin 2015; Ling et al. 2015 [EMNLP]; Plank, SÃ¸gaard and Goldberg 2016...).
answer:

en