Dr Shoaib Jameel
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Email
shoaib.jameel@essex.ac.uk -
Location
5A.529, Colchester Campus
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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
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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.
Natural Language Processing (NLP)
The goal is to propose novel methods in NLP.
Teaching and supervision
Current teaching responsibilities
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Team Project Challenge (EE) (CE293)
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Team Project Challenge (WBL) (CE299)
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Information Retrieval (CE306)
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Information Retrieval (CE706)
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Text Analytics (CE807)
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Team Project Challenge (CS) (CE291)
Publications
Journal articles (3)
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 (32)
Zihao, F., Bing, L., Wai, L. and Jameel, MS., Dynamic Topic Tracker for KB-to-Text Generation
Correia, A., Jameel, MS., Schneider, D., Paredes, H. and Fonseca, B., A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled Scientometrics
Jameel, S. and Schockaert, S., (2020). Word and document embedding with VMF-mixture priors on context word vectors
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
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
(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
2020
RoleCatcher AI
Fintex
Mersea Homes KTP application
Innovate UK (formerly Technology Strategy Board)
Contact
Academic support hours:
Tuesday: 12:00 PM until 1:00 PM (Zoom ID: 97561813599)