Bayesian modeling of manner and path psychological data

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 106-110).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.How people and computers can learn the meaning of words has long been a key question for both AI and cognitive science. It is hypothesized that a person acquires a bias to favor the characteristics of their native language, in order to aid word learning. Other hypothesized aids are syntactic bootstrapping, in which the learner assumes that the meaning of a novel word is similar to that of other words used in a similar syntax, and its complement, semantic bootstrapping, in which the learner assumes that the syntax of a novel word is similar to that of other words used in similar situations. How these components work together is key to understanding word learning. Using cognitive psychology and computer science as a platform, this thesis attempts to tackle these questions using the classic example of manner and path verb bias. A series of cognitive psychology experiments was designed to gather information on this bias. Considerable flexibility of the subject's bias was demonstrated during these experiments. Another separate series of experiments was conducted using different syntactic frames for the novel verbs to address the question of bootstrapping. The resulting information was used to design a Bayesian model which successfully predicts the human behavior in the psychological experiments that were conducted. Dynamic parameters were required to account for subjects revising their expected manner and path verb distributions during the course of an experiment. Bayesian model parameters that were optimized for rich syntactic frame data performed equally well in predicting poor syntactic frame data.by Catherine Andrea Havasi.M.Eng

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies

Learning Colour Representations of Search Queries

Image search engines rely on appropriately designed ranking features that capture various aspects of the content semantics as well as the historic popularity. In this work, we consider the role of colour in this relevance matching process. Our work is motivated by the observation that a significant fraction of user queries have an inherent colour associated with them. While some queries contain explicit colour mentions (such as 'black car' and 'yellow daisies'), other queries have implicit notions of colour (such as 'sky' and 'grass'). Furthermore, grounding queries in colour is not a mapping to a single colour, but a distribution in colour space. For instance, a search for 'trees' tends to have a bimodal distribution around the colours green and brown. We leverage historical clickthrough data to produce a colour representation for search queries and propose a recurrent neural network architecture to encode unseen queries into colour space. We also show how this embedding can be learnt alongside a cross-modal relevance ranker from impression logs where a subset of the result images were clicked. We demonstrate that the use of a query-image colour distance feature leads to an improvement in the ranker performance as measured by users' preferences of clicked versus skipped images.Comment: Accepted as a full paper at SIGIR 202

Meeting the Computer Halfway: Language Processing in the Artifical Language Lojban

There is one big problem that any natural language researcher faces: all that pesky natural language. In constructing a natural language representation of English, or any other natural language, one soon gets bogged down i