Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021

Usama Yaseen, Stefan Langer


Abstract
This paper presents our findings from participating in the SMM4H Shared Task 2021. We addressed Named Entity Recognition (NER) and Text Classification. To address NER we explored BiLSTM-CRF with Stacked Heterogeneous embeddings and linguistic features. We investigated various machine learning algorithms (logistic regression, SVM and Neural Networks) to address text classification. Our proposed approaches can be generalized to different languages and we have shown its effectiveness for English and Spanish. Our text classification submissions have achieved competitive performance with F1-score of 0.46 and 0.90 on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively. In the case of NER, our submissions scored F1-score of 0.50 and 0.82 on ADE Span Detection (Task 1b) and Profession span detection (Task 7b) respectively.
Anthology ID:
2021.smm4h-1.14
Volume:
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
83–87
Language:
URL:
https://aclanthology.org/2021.smm4h-1.14
DOI:
10.18653/v1/2021.smm4h-1.14
Bibkey:
Cite (ACL):
Usama Yaseen and Stefan Langer. 2021. Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 83–87, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021 (Yaseen & Langer, SMM4H 2021)
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PDF:
https://aclanthology.org/2021.smm4h-1.14.pdf