Negation cues detection using CRF on Spanish product review texts

This article describes the negation cue detection approach designed and built by UPC's team participating in NEGES 2018 Workshop on Negation in Spanish. The approach uses supervised CRFs as the base for training the model with several features engineered to tackle the task of negation cue detection in Spanish. The result is evaluated by the means of precision, recall, and F1 score in order to measure the performance of the approach. The approach was ranked in 1st position in the official testing results with average precision around 91%, average recall around 82%, and average F1 score around 86%. © 2018 CEUR-WS. All rights reserved.Postprint (published version

Semi-supervised learning for disabilities detection on English and Spanish biomedical text

This paper describes the disability detection model approaches presented by UPC’s TALP 3 team for the DIANN 2018 shared task. The best of those approaches was ranked in 3rd place for exact-matching of disability detection. The models combine a semi-supervised learning model using CRFs and LSTM with word embedding features with a supervised CRF model for the detection of disabilities and negations respectively.Peer ReviewedPostprint (published version

Negation cues detection using CRF on Spanish product review texts

This article describes the negation cue detection approach designed and built by UPC's team participating in NEGES 2018 Workshop on Negation in Spanish. The approach uses supervised CRFs as the base for training the model with several features engineered to tackle the task of negation cue detection in Spanish. The result is evaluated by the means of precision, recall, and F1 score in order to measure the performance of the approach. The approach was ranked in 1st position in the official testing results with average precision around 91%, average recall around 82%, and average F1 score around 86%. © 2018 CEUR-WS. All rights reserved

Negation cues detection using CRF on Spanish product review texts

This article describes the negation cue detection approach designed and built by UPC's team participating in NEGES 2018 Workshop on Negation in Spanish. The approach uses supervised CRFs as the base for training the model with several features engineered to tackle the task of negation cue detection in Spanish. The result is evaluated by the means of precision, recall, and F1 score in order to measure the performance of the approach. The approach was ranked in 1st position in the official testing results with average precision around 91%, average recall around 82%, and average F1 score around 86%. © 2018 CEUR-WS. All rights reserved

Semi-supervised learning for disabilities detection on English and Spanish biomedical text

This paper describes the disability detection model approaches presented by UPC’s TALP 3 team for the DIANN 2018 shared task. The best of those approaches was ranked in 3rd place for exact-matching of disability detection. The models combine a semi-supervised learning model using CRFs and LSTM with word embedding features with a supervised CRF model for the detection of disabilities and negations respectively.Peer Reviewe