You will be given a definition of a task first, then some input of the task.
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].

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 They use a version of the BNC annotated with UD POS and in which words have been replaced by lemmas.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, Sgaard and Goldberg 2016...).
Output:
en