Outputs (27)

Can we predict QPP? An approach based on multivariate outliers (2024)
Presentation / Conference Contribution
Chifu, A., Déjean, S., Garouani, M., Mothe, J., Ortiz, D., & Ullah, M. Z. (2024, March). Can we predict QPP? An approach based on multivariate outliers. Presented at 46th European Conference on Information Retrieval, ECIR 2024, Glasgow

Query performance prediction (QPP) aims to predict the success and failure of a search engine on a collection of queries and documents. State of the art predictors can enable this prediction with a degree of accuracy; however, it is far from being pe... Read More about Can we predict QPP? An approach based on multivariate outliers.

Combining Word Embedding Interactions and LETOR Feature Evidences for Supervised QPP (2023)
Presentation / Conference Contribution
Datta, S., Ganguly, D., Mothe, J., & Ullah, M. Z. (2023, April). Combining Word Embedding Interactions and LETOR Feature Evidences for Supervised QPP. Presented at 45th European Conference on Information Retrieval (ECIR), Dublin, Ireland

In information retrieval, query performance prediction aims to predict whether a search engine is likely to succeed in retrieving potentially relevant documents to a user's query. This problem is usually cast into a regression problem where a machine... Read More about Combining Word Embedding Interactions and LETOR Feature Evidences for Supervised QPP.

Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction (2023)
Journal Article
Aziz, A., Hossain, M. A., Chy, A. N., Ullah, M. Z., & Aono, M. (2023). Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction. Natural Language Processing Journal, 5, Article 100039. https://doi.org/10.1016/j.nlp.2023.100039

Lexical complexity prediction (LCP) determines the complexity level of words or phrases in a sentence. LCP has a significant impact on the enhancement of language translations, readability assessment, and text generation. However, the domain-specific... Read More about Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction.

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations (2023)
Journal Article
Mothe, J., & Ullah, M. Z. (2024). Selective Query Processing: A Risk-Sensitive Selection of Search Configurations. ACM transactions on information systems, 42(1), https://doi.org/10.1145/3608474

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, how... Read More about Selective Query Processing: A Risk-Sensitive Selection of Search Configurations.

InnEO'Space PhD: Preparing Young Researchers for a successful career on Earth Observation applications (2022)
Presentation / Conference Contribution
Mothe, J., Bayer, A., Castello, V., Ciaccio, V., Del Frate, F., De Santis, D., Ivanovici, M., Lehuerou Kerisel, A., Necşoi, D., Nzeh Ndong, A., Neptune, N., Perier-Camby, M., Recchioni, M., Ullah, M. Z., & Voinea, M. (2021, September). InnEO'Space PhD: Preparing Young Researchers for a successful career on Earth Observation applications. Presented at International Conference on Innovation in Aviation & Space to the Satisfaction of the European Citizens (11th EASN 2021), Salerno

InnEO'Space PhD project is preparing young researchers for a successful career by developing modernised and transferable PhD courses and learning resources based on innovation skills and employers' needs as well as in-depth knowledge of high stakes a... Read More about InnEO'Space PhD: Preparing Young Researchers for a successful career on Earth Observation applications.

Comparison of machine learning models for early depression detection from users’ posts (2022)
Book Chapter
Mothe, J., Ramiandrisoa, F., & Ullah, M. Z. (2022). Comparison of machine learning models for early depression detection from users’ posts. In F. Crestani, D. E. Losada, & J. Parapar (Eds.), Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project (111-139). Springer. https://doi.org/10.1007/978-3-031-04431-1_5

With around 300 millions people worldwide suffering from depression, the detection of this disorder is crucial and a challenge for individual and public health. As with many diseases, early detection means better medical management; the use of social... Read More about Comparison of machine learning models for early depression detection from users’ posts.

Instruments and Tools to Identify Radical Textual Content (2022)
Journal Article
Mothe, J., Ullah, M. Z., Okon, G., Schweer, T., Juršėnas, A., & Mandravickaitė, J. (2022). Instruments and Tools to Identify Radical Textual Content. Information, 13(4), Article 193. https://doi.org/10.3390/info13040193

The Internet and social networks are increasingly becoming a media of extremist propaganda. On homepages, in forums or chats, extremists spread their ideologies and world views, which are often contrary to the basic liberal democratic values of the E... Read More about Instruments and Tools to Identify Radical Textual Content.

Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach (2021)
Presentation / Conference Contribution
Mothe, J., & Ullah, M. Z. (2021, November). Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach. Presented at 30th ACM International Conference on Information & Knowledge Management, Queensland, Australia

A search engine generally applies a single search strategy to any user query. The search combines many component processes (e.g., indexing, query expansion, search-weighting model, document ranking) and their hyperparameters, whose values are optimiz... Read More about Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach.

Exploiting various word embedding models for query expansion in microblog (2020)
Presentation / Conference Contribution
Ahmed, S., Chy, A. N., & Ullah, M. Z. (2020). Exploiting various word embedding models for query expansion in microblog. In 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC). https://doi.org/10.1109/R10-HTC49770.2020.9357016

Microblogs, especially Twitter, make it easier to communicate with others in a real-time manner and is treated as a valuable information source. With the increasing amount of tweets, it would be fascinating to be able to extract essential information... Read More about Exploiting various word embedding models for query expansion in microblog.

An ML Model for Predicting Information Check-Worthiness using a Variety of Features (2020)
Presentation / Conference Contribution
Ullah, M. Z. (2020). An ML Model for Predicting Information Check-Worthiness using a Variety of Features. In Proceedings of the Workshop on Machine Learning for Trend and Weak Signal Detection in Social Networks and Social Media (56-61)

In this communication, we introduce the important problem of information check-worthiness. We present the method we developed to automatically answer this problem. This method makes use of an elaborated information representation that combines the “i... Read More about An ML Model for Predicting Information Check-Worthiness using a Variety of Features.