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].

[Q]: Este es un trabajo interesante, donde el autor quiere mostrar a las Honeynets como medida de seguridad alternativa, para aprendizaje del  modo de operar de intrusos y así crear nuevas formas de protección.  Observaciones:  - El nombre del único autor debe estar centralizado en el artículo. - Palabras y figuras en inglés. El artículo está en español. - Las referencias están inadecuadas: hay  un único artículo citado y con referencia incompleta. Mejorar la revisión bibliográfica, citando artículos y libros  actuales.
[A]: es


[Q]: Review: Multimodal Word Distributions  - Strengths:  Overall a very strong paper.  - Weaknesses: The comparison against similar approaches could be extended.  - General Discussion:  The main focus of this paper is the introduction of a new model for learning multimodal word distributions formed from Gaussian mixtures for multiple word meanings. i. e. representing a word by a set of many Gaussian distributions.  The approach, extend the model introduced by Vilnis and McCallum (2014) which represented word as unimodal Gaussian distribution. By using a multimodal, the current approach attain the problem of polysemy.  Overall, a very strong paper, well structured and clear. The experimentation is correct and the qualitative analysis made in table 1 shows results as expected from the approach.  There’s not much that can be faulted and all my comments below are meant to help the paper gain additional clarity.   Some comments:   _ It may be interesting to include a brief explanation of the differences between the approach from Tian et al. 2014 and the current one. Both split single word representation into multiple prototypes by using a mixture model.   _ There are some missing citations that could me mentioned in related work as :  Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space Neelakantan, A., Shankar. J. Passos, A., McCallum. EMNLP 2014 Do Multi-Sense Embeddings Improve Natural Language Understanding? Li and Jurafsky, EMNLP 2015 Topical Word Embeddings. Liu Y., Liu Z., Chua T.,Sun M. AAAI 2015  _ Also, the inclusion of the result from those approaches in tables 3 and 4 could be interesting.   _ A question to the authors: What do you attribute the loss of performance of w2gm against w2g in the analysis of SWCS?  I have read the response.
[A]: en


[Q]: Este artículo presenta un  Sistema de Reservas de Productos Turísticos mediante Servicios WEB y Dispositivos Móviles. Son utilizadas herramientas de dominio público y el desempeño es verificado  usando simulación. Considerando que los modelos no son perfectos, sobre todo  por la dificultad de modelar el comportamiento  humano, la realización de un estudio de caso (un piloto en escala reducida), donde se pueda medir el beneficio real de la herramienta propuesta.
[A]:
es