People

Dr Mohammed Jameel

Visiting Fellow
School of Computer Science and Electronic Engineering (CSEE)
Dr Mohammed Jameel
  • Email

  • Location

    5A.529, Colchester Campus

  • Academic support hours

    Tuesday: 12:00 PM until 1:00 PM (Zoom ID: 97561813599)

Profile

Biography

My work revolves around proposing novel computational methods for machine learning with applications to text mining. Specifically, my work has centred around learning low-dimensional representations of natural language text on a large-scale. Among others, I have developed a variety of probabilistic topic models, which have seen applications in text mining and information retrieval, as well as vector space embeddings, which have shown promising results in tasks such as knowledge base completion and commonsense reasoning. You can find more about me here: https://bashthebuilder.github.io/

Qualifications

  • PhD The Chinese University of Hong Kong, (2014)

Research and professional activities

Research interests

Information Retrieval (IR)

The goal is to propose novel computational methods for ad-hoc IR using one of these machine learning methods.

Key words: Document Content Analysis
Open to supervise

Natural Language Processing (NLP)

The goal is to propose novel methods in NLP.

Key words: Deep Learning for NLP
Open to supervise

Teaching and supervision

Previous supervision

Mahsa Abazari Kia
Mahsa Abazari Kia
Thesis title: Question-Driven Text Summarization with Extractive-Abstractive Frameworks.
Degree subject: Computer Science
Degree type: Doctor of Philosophy
Awarded date: 24/4/2023

Publications

Journal articles (13)

Zogan, H., Razzak, I., Jameel, S. and Xu, G., (2024). Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic. IEEE Journal of Biomedical and Health Informatics, 1-9

He, L., Xu, G., Jameel, S., Wang, X. and Chen, H., (2023). Graph-Aware Deep Fusion Networks for Online Spam Review Detection. IEEE Transactions on Computational Social Systems. 10 (5), 2557-2565

Roy, S., Gaur, V., Raza, H. and Jameel, S., (2023). CLEFT: Contextualised Unified Learning of User Engagement in Video Lectures with Feedback. IEEE Access. 11, 17707-17720

Correia, A., Grover, A., Jameel, S., Schneider, D., Antunes, P. and Fonseca, B., (2023). A hybrid human–AI tool for scientometric analysis. Artificial Intelligence Review. 56 (S1), 983-1010

Correia, A., Guimarães, D., Paredes, H., Fonseca, B., Paulino, D., Trigo, L., Brazdil, P., Schneider, D., Grover, A. and Jameel, S., (2023). NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity Detection. IEEE Transactions on Human-Machine Systems. 53 (6), 1017-1026

Zogan, H., Razzak, I., Wang, X., Jameel, S. and Xu, G., (2022). Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web. 25 (1), 281-304

Kia, MA., Garifullina, A., Kern, M., Chamberlain, J. and Jameel, S., (2022). Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion. IEEE Access. 10, 45080-45092

Zhou, J., Zogan, H., Yang, S., Jameel, S., Xu, G. and Chen, F., (2021). Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia. IEEE Transactions on Computational Social Systems. 8 (4), 982-991

Zhang, JJ., Wang, F-Y., Yuan, Y., Xu, G., Liu, H., Gao, W., Jameel, S., Razzak, I., Eklund, P., Ahmed, S., Qin, R., Li, J., Wang, X., Yang, D-N., Turgut, D., Benslimane, A., Prasad, N. and Chen, K-C., (2021). Guest Editorial Computational Social Systems for COVID-19 Emergency Management and Beyond. IEEE Transactions on Computational Social Systems. 8 (4), 928-929

Benayas, A., Hashempour, R., Rumble, D., Jameel, S. and De Amorim, RC., (2021). Unified Transformer Multi-Task Learning for Intent Classification With Entity Recognition. IEEE Access. 9, 147306-147314

Jameel, S., Lam, W. and Bing, L., (2015). Supervised topic models with word order structure for document classification and retrieval learning. Information Retrieval Journal. 18 (4), 283-330

Bing, L., Jiang, S., Lam, W., Zhang, Y. and Jameel, S., (2015). Adaptive Concept Resolution for document representation and its applications in text mining. Knowledge-Based Systems. 74, 1-13

Bing, L., Lam, W., Wong, T-L. and Jameel, S., (2015). Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context. ACM Transactions on Information Systems. 33 (2), 1-38

Conferences (45)

Talebpour, M., Garcia Seco De Herrera, A. and Jameel, S., (2023). Topics in Contextualised Attention Embeddings

Barry, E., Jameel, S. and Raza, H., (2022). Emojional: Emoji Embeddings

Yan, K., Zhang, C., Hou, J., Wang, P., Bouraoui, Z., Jameel, S. and Schockaert, S., (2022). Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention

Yan, K., Bouraoui, Z., Wang, P., Jameel, S. and Schockaert, S., (2021). Few-shot image classification with multi-facet prototypes

Antonio, C., Diogo, G., Dennis, P., Jameel, MS., Daniel, S., Benjamin, F. and Hugo, P., (2021). AuthCrowd: Author Name Disambiguation and Entity Matching using Crowdsourcing

Correia, A., Paulino, D., Paredes, H., Fonseca, B., Jameel, S., Schneider, D. and de Souza, JM., (2021). Scientometric Research Assessment of IEEE CSCWD Conference Proceedings: An Exploratory Analysis from 2001 to 2019

Hamad, Z., Imran, R., Jameel, MS. and Guandong, X., (2021). DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media

Kun, Y., Zied, B., Ping, W., Jameel, MS. and Steven, S., (2021). Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning

He, L., Chen, H., Wang, D., Jameel, MS., Yu, P. and Xu, G., (2021). Click-Through Rate Prediction with Multi-Modal Hypergraphs

Correia, A., Fonseca, B., Paredes, H., Chaves, R., Schneider, D. and Jameel, S., (2021). Determinants and Predictors of Intentionality and Perceived Reliability in Human-AI Interaction as a Means for Innovative Scientific Discovery

Jameel, S. and Schockaert, S., (2020). Word and document embedding with VMF-mixture priors on context word vectors

Zihao, F., Bing, L., Wai, L. and Jameel, MS., (2020). Dynamic Topic Tracker for KB-to-Text Generation

Correia, A., Jameel, MS., Schneider, D., Paredes, H. and Fonseca, B., (2020). A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled Scientometrics

Correia, A., Jameel, S., Schneider, D., Fonseca, B. and Paredes, H., (2020). Theoretical underpinnings and practical challenges of crowdsourcing as a mechanism for academic study

Correia, A., Fonseca, B., Paredes, H., Schneider, D. and Jameel, S., (2019). Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains

Correia, A., Paredes, H., Schneider, D., Jameel, S. and Fonseca, B., (2019). Towards Hybrid Crowd-AI Centered Systems: Developing an Integrated Framework from an Empirical Perspective

Camacho-Collados, J., Espinosa-Anke, L., Jameel, S. and Schockaert, S., (2019). A Latent Variable Model for Learning Distributional Relation Vectors

Correia, A., Jameel, S., Schneider, D., Fonseca, B. and Paredes, H., (2019). The Effect of Scientific Collaboration on CSCW Research: A Scientometric Study

Jameel, S., Fu, Z., Shi, B., Lam, W. and Schockaert, S., (2019). Word embedding as maximum a posteriori estimation

Jameel, S., Bouraoui, Z. and Schockaert, S., (2018). Unsupervised Learning of Distributional Relation Vectors

Bouraoui, Z., Jameel, S. and Schockaert, S., (2018). Relation induction in word embeddings revisited

JAMEEL, S. and SCHOCKAERT, S., (2017). Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding

Shi, B., Lam, W., Jameel, S., Schockaert, S. and Lai, KP., (2017). Jointly Learning Word Embeddings and Latent Topics

Jameel, S., Bouraoui, Z. and Schockaert, S., (2017). MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, S. and Schockaert, S., (2016). D-GloVe: A feasible least squares model for estimating word embedding densities

Jameel, S. and Schockaert, S., (2016). Entity embeddings with conceptual subspaces as a basis for plausible reasoning

Schockaert, S. and Jameel, S., (2016). Plausible reasoning based on qualitative entity embeddings

Liao, Y., Lam, W., Jameel, S., Schockaert, S. and Xie, X., (2016). Who Wants to Join Me?

Jameel, S., Liao, Y., Lam, W., Schockaert, S. and Xie, X., (2016). Exploring Urban Lifestyles Using a Nonparametric Temporal Graphical Model

Liu, P., Jameel, S., Wu, KK. and Meng, H., (2016). Learning Track Representation and Trends for Conference Analytics

Jameel, S., Lam, W., Schockaert, S. and Bing, L., (2015). A Unified Posterior Regularized Topic Model with Maximum Margin for Learning-to-Rank

Liao, Y., Jameel, S., Lam, W. and Xie, X., (2015). Abstract Venue Concept Detection from Location-Based Social Networks

Jameel, S., Lam, W. and Bing, L., (2015). Nonparametric Topic Modeling Using Chinese Restaurant Franchise with Buddy Customers

Liu, P., Jameel, S., Lam, W., Ma, B. and Meng, H., (2015). Topic modeling for conference analytics

Bing, L., Lam, W., Jameel, S. and Lu, C., (2014). Website Community Mining from Query Logs with Two-Phase Clustering

Jameel, S. and Lam, W., (2013). A Nonparametric N-Gram Topic Model with Interpretable Latent Topics

Jameel, S. and Lam, W., (2013). An unsupervised topic segmentation model incorporating word order

Jameel, S. and Lam, W., (2013). An N-Gram Topic Model for Time-Stamped Documents

Jameel, S., Lam, W. and Qian, X., (2012). Ranking Text Documents Based on Conceptual Difficulty Using Term Embedding and Sequential Discourse Cohesion

Jameel, S., Qian, X. and Lam, W., (2012). N-gram fragment sequence based unsupervised domain-specific document readability

Jameel, S. and Qian, X., (2012). An Unsupervised Technical Readability Ranking Model by Building a Conceptual Terrain in LSI

Jameel, S., Lam, W., Qian, X. and Au Yeung, C-M., (2012). An unsupervised technical difficulty ranking model based on conceptual terrain in the latent space

Jameel, S., Lam, W., Au Yeung, C-M. and Chyan, S., (2011). An unsupervised ranking method based on a technical difficulty terrain

Jameel, MS., Akshat, A. and Singh, CT., (2008). Enhancements in query evaluation and page summarization of The Thinking Algorithm

(2006). Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop on - COLING ACL '06

Grants and funding

2021

Multi-Modal Image Style Transfer: Automatically Geo-Localising Reading Material Digital Artwork for Increased Reader Engagement

University of Essex (GCRF)

2020

RoleCatcher AI

Fintex

Contact

shoaib.jameel@essex.ac.uk

Location:

5A.529, Colchester Campus

Academic support hours:

Tuesday: 12:00 PM until 1:00 PM (Zoom ID: 97561813599)

More about me

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