Nabiha Asghar | University of Waterloo, Canada (original) (raw)

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Papers by Nabiha Asghar

Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various bus... more Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Automatic extraction of cause-effect relationships from natural language texts is a challenging o... more Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on small and domain-specific data sets. However, with the advent of big data, the availability of affordable computing power and the recent popularization of machine learning, the paradigm to tackle this problem has slowly shifted. Machines are now expected to learn generic causal extraction rules from labelled data with minimal supervision, in a domain independent-manner. In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.

Affect Control Theory (ACT) is a mathematically well-defined model that makes accurate prediction... more Affect Control Theory (ACT) is a mathematically well-defined model that makes accurate predictions about the affective content of human action. The affective predictions, which are derived from statistics about human actions and identities in real and laboratory environments, are shared normative behaviours that are believed to lead to solutions to everyday cooperative problems. A probabilistic and decision-theoretic generalisation of ACT, called BayesAct, allows the principles of ACT to be used for human-interactive agents by defining a utility function and a probabilistic version of the ACT dynamical model of affect. Planning in BayesAct, which we address in this paper, then allows one to go beyond the affective norm, and leads to the emergence of more complex interactions between “cognitive” reasoning and “affective” reasoning, such as deception leading to manipulation and altercasting. As BayesAct is a large hybrid (continuous-discrete) state/action/observation partially observable Markov decision process (POMDP), in this paper we propose a continuous variant of a successful Monte-Carlo tree search planner (POMCP), which performs dynamic discretisation of the action and observation spaces while planning. We demonstrate our variant POMCP-C in simulation on (i) two two-agent coordination problems that involves manipulation through affective interaction, and (ii) an affectively-aware assistive health-care device. In addition, we show that our solver can be used in non-affective domains, by demonstrating it on a continuous robot
navigation problem from the literature and achieving over 50% increase in average reward compared to traditional solvers.

Affect Control Theory (ACT) is a mathematical model that makes accurate predictions about human b... more Affect Control Theory (ACT) is a mathematical model that makes accurate predictions about human behaviour across a wide range of settings. The predictions, which are derived from statistics about human actions and identities in real and laboratory environments, are shared prescriptive and affective behaviours that are believed to lead to solutions to everyday cooperative problems. A generalisation of ACT, called BayesAct, allows the principles of ACT to be used for human-interactive agents by combining a probabilistic version of the ACT dynamical model of affect with a utility function encoding external goals.
Planning in BayesAct, which we address in this paper, then allows one to go beyond the affective prescription, and leads to the emergence of more complex interactions between “cognitive” and “affective” reasoning, such as deception leading to manipulation and altercasting. We use a continuous variant of a successful Monte-Carlo tree search planner (POMCP) that dynamically discretises the action and observation spaces while planning. We give demonstrations on two classic two-person social dilemmas.

Thesis Chapters by Nabiha Asghar

Grotzsch’s Theorem is one of the most famous theorems in graph colouring theory. Its original pro... more Grotzsch’s Theorem is one of the most famous theorems in graph colouring theory. Its original proof, given in German, in 1958, was fairly complex. In 1989, Steinberg and Younger [17] gave the first correct proof, in English, of the dual version of this theorem. This essay studies the Steinberg-Younger proof in detail, putting special emphasis on improved
presentation of their arguments and clarity of exposition. It also gives a new, much simpler proof that is inspired by Carsten Thomassen’s [19], but is due to an unpublished work of C. Nunes da Silva, R.B. Richter and D. Younger.