2021
Christos Smailis Mahsa Shafaei, Ioannis Kakadiaris
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 2021.
Abstract | Links | BibTeX | Tags: Deeplearning
@conference{(RANLP2021),
title = {A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers},
author = {Mahsa Shafaei, Christos Smailis, Ioannis Kakadiaris, Thamar Solorio},
url = {https://aclanthology.org/2021.ranlp-1.146.pdf},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},
pages = {1297-1307},
abstract = {In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers. First, we introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube, along with their corresponding age-suitability rating labels. Secondly, we propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem. This is the first attempt to combine video, audio, and speech information for this problem, and our experimental results show that multi-modal approaches significantly outperform the best mono and bimodal models in this task.},
keywords = {Deeplearning},
pubstate = {published},
tppubtype = {conference}
}
2017
Suraj Maharjan Gustavo Aguilar, A. Pastor López Monroy
A Multi-task Approach for Named Entity Recognition on Social Media Data Inproceedings
In: Proceedings of 3rd Workshop on Noisy User-generated Text, WNUT 2017., 2017, (Ranked 1st place in the two evaluation metrics).
Abstract | Links | BibTeX | Tags: CRF, Deeplearning, Multitask, NER
@inproceedings{aguilar-EtAl:2017:WNUT,
title = {A Multi-task Approach for Named Entity Recognition on Social Media Data},
author = {Gustavo Aguilar, Suraj Maharjan, A. Pastor López Monroy, Thamar Solorio},
url = {http://www.aclweb.org/anthology/W17-4419},
year = {2017},
date = {2017-09-07},
publisher = {Proceedings of 3rd Workshop on Noisy User-generated Text, WNUT 2017.},
abstract = {Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.},
note = {Ranked 1st place in the two evaluation metrics},
keywords = {CRF, Deeplearning, Multitask, NER},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Samih, Younes; Maharjan, Suraj; Attia, Mohammed; Kallmeyer, Laura; Solorio, Thamar
Multilingual Code-switching Identification via LSTM Recurrent Neural Networks Proceeding
Proceedings of the Second Workshop on Computational Approaches to Code Switching; EMNLP, 2016.
Links | BibTeX | Tags: Code-Switching, CRF, Deeplearning, Neural Networks
@proceedings{Samih2016,
title = {Multilingual Code-switching Identification via LSTM Recurrent Neural Networks},
author = {Younes Samih and Suraj Maharjan and Mohammed Attia and Laura Kallmeyer and Thamar Solorio},
url = {http://www.aclweb.org/anthology/W/W16/W16-5806.pdf},
year = {2016},
date = {2016-10-31},
publisher = {Proceedings of the Second Workshop on Computational Approaches to Code Switching; EMNLP},
keywords = {Code-Switching, CRF, Deeplearning, Neural Networks},
pubstate = {published},
tppubtype = {proceedings}
}