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]: This paper presents an approach to tag word senses with temporal information (past, present, future or atemporal). They model the problem using a graph-based semi-supervised classification algorithm that allows to combine item specific information - such as the presence of some temporal indicators in the glosses - and the structure of Wordnet - that is semantic relations between synsets â, and to take into account unlabeled data. They perform a full annotation of Wordnet, based on a set of training data labeled in a previous work and using the rest of Wordnet as unlabeled data. Specifically, they take advantage of the structure of the label set by breaking the task into a binary formulation (temporal vs atemporal), then using the data labeled as temporal to perform a finer grained tagging (past, present or future). In order to intrinsically evaluate their approach, they annotate a subset of synsets in Wordnet using crowd-sourcing. They compare their system to the results obtained by a state-of-the-art time tagger (Stanford's SUTime) using an heuristic as a backup strategy, and to previous works. They obtain improvements around 11% in accuracy, and show that their approach allows performance higher than previous systems using only 400 labeled data. Finally, they perform an evaluation of their resource on an existing task (TempEval-3) and show improvements of about 10% in F1 on 4 labels.  This paper is well-constructed and generally clear, the approach seems sound and well justified. This work led to the development of a resource with fine grained temporal information at the word sense level that would be made available and could be used to improve various NLP tasks. I have a few remarks, especially concerning the settings of the experiments.  I think that more information should be given on the task performed in the extrinsic evaluation section. An example could be useful to understand what the system is trying to predict (the features describe âentity pairsâ but it has not been made clear before what are these pairs) and what are the features (especially, what are the entity attributes? What is the POS for a pair, is it one dimension or two? Are the lemmas obtained automatically?). The sentence describing the labels used is confusing, I'm not sure to understand what âevent to document creation timeâ and âevent to same sentence eventâ means, are they the kind of pairs considered? Are they relations (as they are described as relation at the beginning of p.8)? I find unclear the footnote about the 14 relations: why the other relations have to be ignored, what makes a mapping too âcomplexâ? Also, are the scores macro or micro averaged? Finally, the ablation study seems to indicate a possible redundancy between Lexica and Entity with quite close scores, any clue about this behavior?  I have also some questions about the use of the SVM.  For the extrinsic evaluation, the authors say that they optimized the parameters of the algorithm: what are these parameters?  And since a SVM is also used within the MinCut framework, is it optimized and how? Finally, if it's the LibSVM library that is used (Weka wrapper), I think a reference to LibSVM should be included.   Other remarks: - It would be interesting to have the number of examples per label in the gold data, the figures are given for coarse grained labels (127 temporal vs 271 atemporal), but not for the finer grained. - It would also be nice to have an idea of the number of words that are ambiguous at the temporal level, words like âpresentâ. - It is said in the caption of the table 3 that the results presented are âsignificantly betterâ but no significancy test is indicated, neither any p-value.  Minor remarks: - Related work: what kind of task was performed in (Filannino and Nenadic, 2014)? - Related work: ârequires a post-calibration procedureâ, needs a reference (and p.4 in 3.3 footnote it would be clearer to explain calibration) - Related work: âtheir model differ from oursâ, in what? - Table 3 is really too small: maybe, remove the parenthesis, put the â(p,r,f1)â in the caption and give only two scores, e.g. prec and f1. The caption should also be reduced. - Information in table 4 would be better represented using a graph. - Beginning of p.7: 1064 â 1264 - TempEval-3: reference ? - table 6: would be made clearer by ordering the scores for one column - p.5, paragraph 3: atemporal) â atemporal
[EX A]: en

[EX Q]: El documento intenta mostrar una evaluación de atributos de calidad de software basada en el estándar ISO 9126. El documento muestra una propuesta de una plantilla con una serie de preguntas para analizar la calidad de diferentes atributos de calidad de un producto software desarrollado. Sin embargo, el documento carece de estructura. La redacción es deficiente. En algunas partes del documento no se siguen los parámetros de formato exigidos en la guía de autores. Por ejemplo, el resumen (abstract) está centrado y según la guía del evento debe estar justificado. De la misma forma se presentan algunos párrafos del documento. En cuanto a la redacción, el documento es difícil de leer, faltan o están mal ubicados los signos de puntuación. Las palabras claves no coinciden como palabras claves del artículo. Faltan referencias bibliográficas que sustenten las afirmaciones que se hacen. A pesar de que la plantilla puede ser tan útil como intentan asegurar en el trabajo, es necesario buscar una validación más consolidada que permita asegurar su utilidad. Las estadísticas presentadas carecen de justificaciones válidas y el formato de presentación es deficiente. Gran parte del documento se centra en hacer afirmaciones descritas en el estándar ISO 1926 y: 1. No se encuentra debidamente citado como referencia principal del trabajo. 2. Centrar el artículo para resumir la definición de cada característica o subcaracterística del estándar no parece muy apropiado para este tipo de trabajos. El formato de presentación de la plantilla carece de claridad. Pareciera que es un formato de cuestionario pero no se muestra como tal. Existe un listado de afirmaciones y preguntas poco claras. Algunas de dichas preguntas son mencionadas en los resultados, sin embargo no aparecen en el listado denominado por la autora como "plantila", es el caso de: ¿El sistema opera junto con otro sistema?. En términos generales el trabajo debe refinar mucho más.
[EX A]: es

[EX Q]: el trabajo mostrado en este paper esta muy bien documentado.  presentan un modelo de extracción de información para la planificación de búsquedas en web.  para ello con algunos casos que prueba el modelo propuesto
[EX A]:
es