Akbar Karimi


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CAISA@SMM4H’22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods
Akbar Karimi | Lucie Flek
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H’22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.

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Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts
Luna De Bruyne | Akbar Karimi | Orphee De Clercq | Andrea Prati | Veronique Hoste
Proceedings of the Thirteenth Language Resources and Evaluation Conference

While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA). Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. The presented dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information. However, when more specific information about the aspects is desired, image recognition could be essential.


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UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model
Akbar Karimi | Leonardo Rossi | Andrea Prati
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique. Since the content is full of toxic words that have not been written according to their dictionary spelling, attendance to individual characters is crucial. Therefore, we use CharacterBERT to extract features based on the word characters. It consists of a CharacterCNN module that learns character embeddings from the context. These are, then, fed into the well-known BERT architecture. The bag-of-words method, on the other hand, further improves upon that by making sure that some frequently used toxic words get labeled accordingly. With a ∼4 percent difference from the first team, our system ranked 36 th in the competition. The code is available for further research and reproduction of the results.

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Improving BERT Performance for Aspect-Based Sentiment Analysis
Akbar Karimi | Leonardo Rossi | Andrea Prati
Proceedings of the Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021)

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AEDA: An Easier Data Augmentation Technique for Text Classification
Akbar Karimi | Leonardo Rossi | Andrea Prati
Findings of the Association for Computational Linguistics: EMNLP 2021

This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code will be made available for further study and reproduction of the results.


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Extracting an English-Persian Parallel Corpus from Comparable Corpora
Akbar Karimi | Ebrahim Ansari | Bahram Sadeghi Bigham
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)