Luhn Revisited: Significant Words Language Models

Our paper "Luhn Revisited: Significant Words Language Models", with Hosein Azarbonyad, Jaap Kamps, Djoerd Hiemstra and Maarten Marx, has been accepted as a long paper at The 25th ACM International Conference on Information and Knowledge Management (CIKM'16). \o/ On of the key factors affecting search quality is the fact that our queries are ultra-short statements […]

On Horizontal and Vertical Separation in Hierarchical Text Classification

Our paper "On Horizontal and Vertical Separation in Hierarchical Text Classification", with Hosein Azarbonyad, Jaap Kamps, and Maarten Marx, has been accepted as a long paper at The ACM International Conference on the Theory of Information Retrieval (ICTIR'16). \o/ Hierarchy is an effective and common way of representing information and many real-world textual data can […]

From Probability Ranking Principle to Strong Separation Principle

Separability is a highly desirable property for constructing and operating autonomous information systems, and especially classifiers. In this post, I present a step by step argument which shows that based on the classification principles, having better separability in the feature space leads to better accuracy in the classification results. Based on the Probability Ranking Principle […]

Building a multi-domain comparable corpus using a learning to rank method

Our paper "Building a multi-domain comparable corpus using a learning to rank method", with Razieh Rahimi, Azadeh Shakery, Javid Dadashkarimi, Mozhdeh Ariannezhad, and Hossein Nasr Esfahani has been published at the Journal of Natural Language Engineering. \o/ Multilingual nature of the Web makes interlingual translation a crucial requirement for information management applications. Bilingual humanly constructed […]

Two-Way Parsimonious Classification Models for Evolving Hierarchies

Our paper "Two-Way Parsimonious Classification Models for Evolving Hierarchies", with Hosein Azarbonyad, Jaap Kamps, and Maarten Marx, has been accepted as a long paper at the Conference and Labs of the Evaluation Forum (CLEF'16). \o/ Modern web data is highly structured in terms of entities and relations from large knowledge bases, geo-temporal references, and social […]

Significant Words Representations of Entities

My doctoral consortium submission on "Significant Words Representations of Entities", has been accepted at the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2016). \o/ Transforming the data into a suitable representation is the first key step of data analysis, and the performance of any data-oriented method is heavily depending on it. […]

Alecsa: Attentive Learning for Email Categorization using Structural Aspects

Our paper "Alecsa: Attentive Learning for Email Categorization using Structural Aspects", with Azadeh Shakery, and Maryam S. Mirian, has been published at the Knowledge-Based Systems Journal. \o/ Due to the enormous volume of email data generated each day, email management has become a vital area of research. Among the email management tasks, automatic email categorization […]

Generalized Group Profiling for Content Customization

Our paper "Generalized Group Profiling for Content Customization", with Hosein Azarbonyad, Jaap Kamps, and Maarten Marx, has been accepted as a short paper at the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR'16). \o/ There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus […]

Parsimonious User and Group Profiling in Venue Recommendation: TREC2015 Contextual Suggestion Track

This year, we participated in the TREC 2015 Contextual Suggestion Track. Contextual suggestion is the task of searching for complex information needs that are highly dependent on both context and user interests. Creating effective profiles for both users and contexts is the main key to build an effective contextual suggestion system. To address these issues, we […]