The democratization of deep learning in TASS 2017

@article{DazGaliano2018TheDO,
  title={The democratization of deep learning in TASS 2017},
  author={Manuel Carlos D{\'i}az-Galiano and Eugenio Mart{\'i}nez-C{\'a}mara and Miguel {\'A}ngel Garc{\'i}a Cumbreras and Manuel Garc{\'i}a Vega and Julio Villena-Rom{\'a}n},
  journal={Proces. del Leng. Natural},
  year={2018},
  volume={60},
  pages={37-44}
}
This research work is partially supported by REDES project (TIN2015-65136-C2-1-R) and SMART project (TIN2017-89517-P) from the Spanish Government, and a grant from the Fondo Europeo de Desarrollo Regional (FEDER). Eugenio Martinez Camara was supported by the Juan de la Cierva Formacion Programme (FJCI-2016-28353) from the Spanish Government. 

Figures and Tables from this paper

Atalaya at TASS 2018: Sentiment Analysis with Tweet Embeddings and Data Augmentation
TLDR
This work presents the participation as team Atalaya in the task of polarity classification of tweets, which followed standard techniques in preprocessing, representation and classification, and also explored some novel ideas.
Emotion Detection for Spanish with Data Augmentation and Transformer-Based Models
TLDR
The participation of Yeti team in IberLEF EmoEvalEs task, which is based on the Spanish Semantic Analysis in TASS 2020 version, and proposes as separate task for 2021 in IerLEF is described.
Learning Cross-lingual Embeddings from Twitter via Distant Supervision
TLDR
This paper exploits noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications and finds that it also provides key opportunities due to the abundance of code-switching and the existence of a shared vocabulary of emoji and named entities.
Overview of TASS 2019: One More Further for the Global Spanish Sentiment Analysis Corpus
TLDR
This paper summarizes the approaches and the results of the submitted systems of the different groups for each task in the TASS workshop, and proposes a new approach to sentiment analysis at tweet level.
Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision
TLDR
This paper exploits noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications and finds that it also provides key opportunities due to the abundance of code-switching and the existence of a shared vocabulary of emoji and named entities.
XLM-T: A Multilingual Language Model Toolkit for Twitter
TLDR
This paper introduces XLMT, a framework for using and evaluating multilingual language models in Twitter, a modular framework that can easily be extended to additional tasks, as well as integrated with recent efforts also aimed at the homogenization of Twitter-specific datasets.
Overview of TASS 2020: Introducing Emotion Detection
TLDR
This paper summarizes the different approaches of the teams who participated, the key insights of their systems and the results obtained for all the proposed solutions.
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
TLDR
A new strong multilingual baseline consisting of an XLM-R (Conneau et al., 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently tune on a target task is provided.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
TLDR
The main contributions of TweetNLP are an integrated Python library for a modern toolkit supporting social media analysis using various task-specific models adapted to the social domain, and an interactive online demo for codeless experimentation using the authors' models.
...
...

References

SHOWING 1-10 OF 24 REFERENCES
Overview of TASS 2016
TLDR
The TASS 2016 proposed tasks, the description of the corpora used, the participant groups, the results and analysis of them are presented.
TASS 2013 - A Second Step in Reputation Analysis in Spanish
TLDR
This paper fully describes the proposed tasks, the contents, format and main figures of the generated corpus, the participant groups and their different approaches, and, finally, the overall results achieved and lessons learned in these two years.
Applying Recurrent Neural Networks to Sentiment Analysis of Spanish Tweets
TLDR
The participation of the Intelligent Systems Group at Universidad Politécnica de Madrid (UPM) in the Sentiment Analysis workshop focused in Spanish tweets, TASS2017 is presented.
Overview of TASS 2015
TLDR
The TASS 2015 proposed tasks, the contents of the generated corpora, the participant groups and the results and analysis of them are presented.
Classifier Ensembles That Push the State-ofthe-Art in Sentiment Analysis of Spanish Tweets
TLDR
Experimental results show that the proposed JACERONG system is top-ranked on the test set of the InterTASS corpus, according to the accuracy metric, and results indicate that the predictive performance on the whole testSet of the General Corpus of Tass outperforms the best result achieved in the four-label evaluation of the previous edition of TASS, in terms of the Macro-F1 metric.
OEG at TASS 2017 : Spanish Sentiment Analysis of tweets at document level
TLDR
Different parameters and systems were tested in each one of the three corpora released for the task, including different Machine Learning algorithms and morphosyntactic analyses for negation detection, along with the use of lexicons and dedicated preprocessing techniques for detecting and correcting frequent errors and expressions in tweets.
TASS 2014 - The Challenge of Aspect-based Sentiment Analysis
TLDR
The third issue of TASS is described, in which four tasks have been proposed, related to sentiment analysis at entity level, and they are circumscribed on the Social TV phenomenon.
TASS - Workshop on Sentiment Analysis at SEPLN
TLDR
TASS, an experimental evaluation workshop within SEPLN, is described to foster the research in the field of sentiment analysis in social media, specifically focused on Spanish language to promote the application of existing state-of-the-art algorithms and techniques.
TASS - Workshop on Sentiment Analysis at SEPLN
TLDR
TASS, an experimental evaluation workshop within SEPLN, is described to foster the research in the field of sentiment analysis in social media, specifically focused on Spanish language to promote the application of existing state-of-the-art algorithms and techniques.
Dataset Classification Combining Wide-coverage Lexical Resources and Text Features
TLDR
For this task, a classification model was built based on the Lingmotif Spanish lexicon, and combined this with a number of formal text features, both general and CMC-specific, as well as single-word keywords and n-gram keywords, achieving above-average results across all three datasets.
...
...