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: 1) an interesting task, 2) the paper is very clearly written, easy to follow, 3) the created data set may be useful for other researchers, 4) a detailed analysis of the performance of the model.  - Weaknesses: 1) no method adapted from related work for a result comparison 2) some explanations about the uniqueness of the task and discussion on limitations of previous research for solving this problem can be added to emphasize the research contributions further.   - General Discussion: The paper presents supervised and weakly supervised models for frame classification in tweets. Predicate rules are generated exploiting language-based and Twitter behavior-based signals, which are then supplied to the probabilistic soft logic framework to build classification models. 17 political frames are classified in tweets in a multi-label classification task. The experimental results demonstrate the benefit of the predicates created using the behavior-based signals. Please find my more specific comments below:  The paper should have a discussion on how frame classification differs from stance classification. Are they both under the same umbrella but with different levels of granularity?  The paper will benefit from adding a brief discussion on how exactly the transition from long congressional speech to short tweets adds to the challenges of the task. For example, does past research rely on any specific cross-sentential features that do not apply to tweets? Consider adapting the method of a frame classification work on congressional speech (or a stance classification work on any text) to the extent possible due to its limitations on Twitter data, to compare with the results of this work.  It seems “weakly supervised” and “unsupervised” – these two terms have been interchangeably used in the paper (if this is not the case, please clarify in author response). I believe "weakly supervised" is the more technically correct terminology under the setup of this work that should be used consistently throughout. The initial unlabeled data may not have been labeled by human annotators, but the classification does use weak or noisy labels of some sort, and the keywords do come from experts. The presented method does not use completely unsupervised data as traditional unsupervised methods such as clustering, topic models or word embeddings would.    The calculated Kappa may not be a straightforward reflection of the difficulty of frame classification for tweets (lines: 252-253), viewing it as a proof is a rather strong claim. The Kappa here merely represents the annotation difficulty/disagreement. Many factors can contribute to a low value  such as poorly written annotation guidelines, selection of a biased annotator, lack of annotator training etc. (on top of any difficulty of frame classification for tweets by human annotators, which the authors actually intend to relate to). 73.4% Cohen’s Kappa is strong enough for this task, in my opinion, to rely on the annotated labels.   Eq (1) (lines: 375-377) will ignore any contextual information (such as negation or conditional/hypothetical statements impacting the contributing word) when calculating similarity of a frame and a tweet. Will this have any effect on the frame prediction model? Did the authors consider using models that can determine similarity with larger text units such as perhaps using skip thought vectors or vector compositionality methods?    An ideal set up would exclude the annotated data from calculating statistics used to select the top N bi/tri-grams (line: 397 mentions entire tweets data set has been used), otherwise statistics from any test fold (or labeled data in the weakly supervised setup) still leaks into the selection process. I do not think this would have made any difference in the current selection of the bi/tri-grams or results as the size of the unlabeled data is much larger, but would have constituted a cleaner experimental setup.    Please add precision and recall results in Table 4.   Minor: please double check any rules for footnote placements concerning placement before or after the punctuation.
Output: 

 en