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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: - Strengths:  Improves over the state-of-the-art. Method might be applicable for other domains.  - Weaknesses:  Not much novelty in method.  Not quite clear if data set is general enough for other domains.  - General Discussion:  This paper describes a rule-based method for generating additional weakly labeled data for event extraction.  The method has three main stages.  First, it uses Freebase to find important slot fillers for matching sentences in Wikipedia (using all slot fillers is too stringent resulting in too few matches).  Next, it uses FrameNet to to improve reliability of labeling trigger verbs and to find nominal triggers.  Lastly, it uses a multi-instance learning to deal with the noisily generated training data.  What I like about this paper is that it improves over the state-of-the-art on a non-trival benchmark.  The rules involved don't seem too obfuscated, so I think it might be useful for the practitioner who is interested to improve IE systems for other domains.  On the other hand, some some manual effort is still needed, for example for mapping Freebase event types to ACE event types (as written in Section 5.3 line 578).  This also makes it difficult for future work to calibrate apple-to-apple against this paper.              Apart from this, the method also doesn't seem too novel.  Other comments:  - I'm also concern with the generalizability of this method to other   domains.  Section 2 line 262 says that 21 event types are selected   from Freebase.  How are they selected?  What is the coverage on the 33 event types in the ACE data.  - The paper is generally well-written although I have some   suggestions for improvement.              Section 3.1 line 316 uses "arguments liked time, location...".  If you mean roles or arguments, or maybe you want to use actual realizations of time and location as examples.  There are minor typos, for e.g. line 357 is missing a "that", but this is not a major concern I have for this paper.
Output: 

 en