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].

[EX Q]: Este articulo presenta para medir la calidad de los procesos de software. El titulo ya fue algo que me sorprendio. Existen miles de debates en que se entiende por calidad en los procesos de software y los autores no concretan cuales son la base de la que parten. Ademas, la clasificacion que hacen en los apartados desde las etapas de desarrollo, los procesos de desarrollo es algo de lo que existen muchas alternativas en la literatura y los autores se centran en una que parecen dar o tomar como el referente mas adecuado. La vision practica en el caso concreto de INAPI si que es de interes pero creo que hubiera sido mas importante el decir porque se aplican las cosas que intentar definir una vision global y como estandar de los conceptos con los que trabajo. Por otro lado, hay otras propuestas metodologicas para aplicar 9001 en el contexto del software. Creo que habria que potenciar de manera mas adecuada el que se defienda que aporta esta en realidad.
[EX A]: es

[EX Q]: This work gives expected results and creates a structure for further research. I would suggest in addition of compass the use of radio waves for improving localization (WiFi for indoor and GPS for outdoor). In addition, there are some low cost platforms in the market that can be used and study, as for example Ardupilot or other similar systems. I was unable to see clearly enough, in the paper, the use of the camera; algorithms, environment settings, etc.; used for following robot. In Figure 7, it seems that camera trajectory is worse than that calculated with odometry, perhaps due to orientation error, but in my opinion it is not very clear during reading. Finally, I would suggest think if is it there some possible feedback of EKF results to odometry in order to reduce its error.
[EX A]: en

[EX Q]: This paper presents a method for embedding data instances into a low-dimensional space that preserves some form of similarity. Although the paper presents this notion as new, basically every pre-trained embedding (be it auto-encoders or word2vec) has been doing the same: representing items in a low-dimensional space that inherently encodes their similarities. Even when looking at the specific case of word/context embeddings, the method is not novel either: this method is almost identical to one of the similarity functions presented in "A Simple Word Embedding Model for Lexical Substitution" (Melamud et al., 2015). The novelty claim must be more accurate and position itself with respect to existing work. In addition, I think the evaluation could be done better. There are plenty of benchmarks for word embeddings in context, for example: *
[EX A]:
en