A Platform for Generating Educational Activities to Teach English as a Second Language

We present a platform for the generation of educational activities oriented to teaching English as a foreign language. The different activities -- games and language practice exercises -- are strongly based on Natural Language Processing techniques. The platform offers the possibility of playing out-of-the-box games, generated from resources created semi-automatically and then manually curated. It can also generate games or exercises of greater complexity from texts entered by teachers, providing a stage of review and edition of the generated content before use. As a way of expanding the variety of activities in the platform, we are currently experimenting with image and text generation. In order to integrate them and improve the performance of other neural tools already integrated, we are working on migrating the platform to a more powerful server. In this paper we describe the development of our platform and its deployment for end users, discussing the challenges faced and how we overcame them, and also detail our future work plans.

A Crowd-Annotated Spanish Corpus for Humor Analysis

Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data. In this work we present a corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet. It is equally divided between tweets coming from humorous and non-humorous accounts. The inter-annotator agreement Krippendorff's alpha value is 0.5710. The dataset is available for general use and can serve as a basis for humor detection and as a first step to tackle subjectivity.

RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN

This article presents classifiers based on SVM and Convolutional Neural Networks (CNN) for the TASS 2017 challenge on tweets sentiment analysis. The classifier with the best performance in general uses a combination of SVM and CNN. The use of word embeddings was particularly useful for improving the classifiers performance.