@conference {Bayram2022219,
	title = {Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data},
	booktitle = {CLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings},
	year = {2022},
	note = {cited By 1},
	pages = {219-225},
	abstract = {In this shared task, we focus on detecting mental health signals in Reddit users{\textquoteright} posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users{\textquoteright} suicide risk-levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users{\textquoteright} posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to-sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task-B, our two models utilize the BERT emotion/sentiment scores. The first computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users{\textquoteright} suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores. Our results outperformed most participating teams and ranked in the top three in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores. {\textcopyright} 2022 Association for Computational Linguistics.},
	keywords = {Auto encoders, Emotion Recognition, Learning systems, Logistics regressions, Long short-term memory, Machine learning models, Mental health, N-grams, Participating teams, Risk assessment, Risk levels, Sentiment scores, Social media datum, Social networking (online)},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137975576\&partnerID=40\&md5=3326dfc2470d3574a556dc3a88778a91},
	author = {Bayram, U. and Benhiba, L.}
}
@conference {Rassam202278,
	title = {Fuzzy Logic-based N-gram Graph Technique for Evaluating Textual Documents Indexes},
	booktitle = {2022 4th International Conference on Computer Communication and the Internet, ICCCI 2022},
	year = {2022},
	note = {cited By 0},
	pages = {78-82},
	abstract = {Fuzzy logic is a term that is used more and more often by researchers specialized in information retrieval and indexing. It is supposed to fill the gaps of the current domain by adding meaning to the information. In this article, the main goal is introducing the fuzzy logic approach in the field of the representation of a corpus of documents, more precisely in the proposal of a new method based on fuzzy functions that allow the evaluation of the generated indexes of a specific corpus.The evaluation is based on calculating the degrees of relevance of a term in relation to another whether it is in the same document of the corpus or in two or more completely different documents of the same corpus. {\textcopyright} 2022 IEEE.},
	keywords = {{\textquoteright}current, Computer circuits, Degree of relevance, Fuzzy function, Fuzzy logic, Fuzzy logic approach, Fuzzy-Logic, Graph technique, Indexing, Indexing (of information), N-grams, Textual documents},
	doi = {10.1109/ICCCI55554.2022.9850268},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137083735\&doi=10.1109\%2fICCCI55554.2022.9850268\&partnerID=40\&md5=3d771186a1a254dd249e3647a64918b2},
	author = {Rassam, L. and Aldiebesghanem, C. and Zellou, A. and Lahmar, E.B.}
}
