TASK DEFINITION: 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].
PROBLEM: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.

SOLUTION: en

PROBLEM: El artículo presenta el diseño de un prototipo que implementa un sistema para la adquisición de datos mediante dispositivos móviles inalámbricos, aplicado a la minería, específicamente en la monitorización de material particulado. Resulta interesante la aplicación de la tecnología móvil a un problema concreto de la industria minera chilena, además el artículo describe de forma muy clara la problemática y el prototipo propuesto.  El artículo resulta novedoso ya que se plantea el uso de la computación (procesamiento de de datos) y de las tecnologías de dispositivos móviles aplicadas a un problema concreto de la industria minera chilena.  El problema, la tecnología presentada (BUG) y el prototipo propuesto están descritos de forma clara.  Resulta relevante la aplicación  de tecnologías computacionales móviles actuales en sectores industriales importantes del país.  El título principal y el resumen deben estar escritos en español y en inglés (sólo vienen en español). Además, el artículo presenta una lista de 29 referencias, pero sólo hace referencia  a una (la primera de la lista). Incluso aparece en las Referencias PCFactory, y no queda muy  claro el porqué.

SOLUTION: es

PROBLEM: - Strengths: This paper presents an extension of many popular methods for learning vector representations of text.  The original methods, such as skip-gram with negative sampling, Glove, or other PMI based approaches currently use word cooccurrence statistics, but all of those approaches could be extended to n-gram based statistics.  N-gram based statistics would increase the complexity of every algorithm because both the vocabulary of the embeddings and the context space would be many times larger.  This paper presents a method to learn embeddings for ngrams with ngram context, and efficiently computes these embeddings.  On similarity and analogy tasks, they present strong results.  - Weaknesses: I would have loved to see some experiments on real tasks where these embeddings are used as input beyond the experiments presented in the paper.  That would have made the paper far stronger.  - General Discussion: Even with the aforementioned weakness, I think this is a nice paper to have at ACL.  I have read the author response.

SOLUTION:
en

