Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape

An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models

Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics. Using this metric, we conducted an empirical analysis of 24 widely used PTLMs. Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm. We observe that our metric correlates with most of the gender-specific metrics in the literature. Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks. We found that prioritizing depth over width, mitigates representational harms in some PTLMs. Our code and data can be found at https://github.com/microsoft/SafeNLP.Comment: 17 pages

Investigation of gateway placement optimization approaches in wireless mesh networks using genetic algorithms

Recently wireless mesh networks (WMNs) gained significant roles in the current communication technologies and have been used in numerous applications such as transportation systems, rescue systems, Surveillance systems, community and neighborhood networking and etc. Therefore, many researchers pay their attention to the wireless mesh network issues especially the gateway placement optimization problems. In this paper, we study and investigate the efforts of many researchers that dealt with the gateway placement optimization problem based on combinatorial optimization concepts in comparison with other conventional algorithms as well as comparing the combinatorial based algorithms with each other. The investigation result shows that the genetic algorithms based approaches on solving gateway optimization problem relatively outperform many other approaches in addition to that the strength of the genetic algorithm depends on the fitness function which is used in measuring the quality of the individuals (fitness value)

Identifying Roles in Social Networks using Linguistic Analysis.

Social media sites have been significantly growing in the past few years. This resulted in the emergence of several communities of communicating groups, and a huge amount of text exchanged between members of those groups. In our work, we study how linguistic analysis techniques can be used for understanding the implicit relations that develop in on-line communities. We use this understanding to develop models that explain the processes that govern language use and how it reveals the formation of social relations. We study the relation between language choices and attitude between participants and how they may lead to or reveal antagonisms and rifts in social groups. Both positive (friendly) and negative (antagonistic) relations exist between individuals in communicating communities. Negative relations have received very little attention, when compared to positive relations, because of the lack of an explicit notion of labeling negative relations in most social computing applications. We alleviate this problem by studying text exchanged between participants to mine their attitude. Another important aspect of our research is the study of influence in discussions and how it affects participants’ discourse. In any debate or discussion, there are certain types of persons who influence other people and affect their ideas and rhetoric. We rely on natural language processing techniques to find implicit connections between individuals that model this influence. We couple this with network analysis techniques for identifying the most authoritative or salient entities. We also study how salience evolves over time. Our work is uniquely characterized by combining linguistic features and network analysis to reveal social roles in different communities. The methods we developed can find several interesting areas of applications. For example, they can be used for identifying authoritative sources in social media, finding influential people in communities, mining attitude toward events and topics, detecting rifts and subgroup formation, summarizing different viewpoints with respect to some topic or entity, and many other such applications.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86271/1/hassanam_1.pd