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Outputs (3)

Exploiting various word embedding models for query expansion in microblog (2020)
Conference Proceeding
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)
Conference Proceeding
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.

Forward and backward feature selection for query performance prediction (2020)
Conference Proceeding
Déjean, S., Ionescu, R. T., Mothe, J., & Ullah, M. Z. (2020). Forward and backward feature selection for query performance prediction. In SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing (690-697). https://doi.org/10.1145/3341105.3373904

The goal of query performance prediction (QPP) is to automatically estimate the effectiveness of a search result for any given query, without relevance judgements. Post-retrieval features have been shown to be more effective for this task while being... Read More about Forward and backward feature selection for query performance prediction.