Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic programming systems. We have implemented the algorithm in a system that allows patterns to have constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains.

Use of technology often has unpleasant side effects, which may include strong, negative emotional states that arise during interaction with computers. Frustration, confusion, anger, anxiety and similar emotional states can affect not only the interaction itself, but also productivity, learning, social relationships, and overall well-being. This paper suggests a new solution to this problem: designing human-computer interaction systems to actively support users in their ability to manage and recover from negative emotional states. An interactive affect-support agent was designed and built to test the proposed solution in a situation where users were feeling frustration. The agent's text-only interaction used components of active listening, empathy, and sympathy, in an effort to support users in their ability to recover from frustration. The agent's effectiveness was evaluated against two control conditions, which were also text-based interactions: (1) users' emotions were ignored, and (...

The recent growth of online information available in the form of natural language documents creates a greater need for computing systems with the ability to process those documents to simplify access to the information. One type of processing appropriate for many tasks is information extraction, a type of text skimming that retrieves specific types of information from text. Although information extraction systems have existed for two decades, these systems have generally been built by hand and contain domain specific information, making them difficult to port to other domains. A few researchers have begun to apply machine learning to information extraction tasks, but most of this work has involved applying learning to pieces of a much larger system. This paper presents a novel rule representation specific to natural language and a learning system, Rapier, which learns information extraction rules. Rapier takes pairs of documents and filled templates indicating the information to be ext...

This paper discusses the issues pertinent to the development of a meaningful social interaction between robots and people through employing degrees of anthropomorphism in a robot's physical design and behaviour. As robots enter our social space, we will inherently project/impose our interpretation on their actions similar to the techniques we employ in rationalising for example, a pet's behaviour. This propensity to anthropomorphise is not seen as a hindrance to social robot development, but rather a useful mechanism that requires judicious examination and employment in social robot research.

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This research was funded in part through fellowships from the Litton and Intel Corporations. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect those of the sponsors.

UNIX programs used to be designed so that they could be connected with pipes created by a shell. This paradigm is insufficient when dealing with many modern programs that demand to be used interactively. expect is a program designed to control interactive programs. expect reads a script that resembles the dialogue itself but which may include multiple paths through it. Scripts include: . send/expect sequences - expect patterns can include regular expressions. . high-level language - Control flow (if/then/else, while, etc.) allows different actions on different inputs, along with procedure definition, built-in expression evaluation, and execution of arbitrary UNIX programs. . job control - Multiple programs can be controlled at the same time. . user interaction - Control can be passed from scripted to interactive mode and vice versa at any time. The user can also be treated as an I/O source/sink. expect successfully deals with interactive programs. It also solves several ot...

In this paper, we argue for embodied conversational characters as the logical extension of the metaphor of human -- computer interaction as a conversation. We argue that the only way to fully model the richness of human faceto -face communication is to rely on conversational analysis that describes sets of conversational behaviors as fulfilling conversational functions, both interactional and propositional. We demonstrate how to implement this approach in Rea, an embodied conversational agent that is capable of both multimodal input understanding and output generation in a limited application domain. Rea supports both social and task-oriented dialogue. We discuss issues that need to be addressed in creating embodied conversational agents, and describe the architecture of the Rea interface. Keywords Conversational Characters, Multimodal Input, Intelligent Agents, Multimodal Output INTRODUCTION The metaphor of face-to-face conversation has been successfully applied to human-interfac...

Over the decades, computational models of human cognition have advanced from programs that produce output similar to that of human problem solvers to systems that mimic both the products and processes of human performance. In this paper, we describe a model that achieves the next step in this progression: predicting individual participants' performance across multiple tasks after estimating a single individual difference parameter. We demonstrate this capability in the context of a model of working memory, where the individual difference parameter for each participant represents working memory capacity. Specifically, our model is able to make zero-parameter predictions of individual participants' performance on a second task after separately fitting performance on a preliminary task. We argue that this level of predictive ability offers an important test of the theory underlying our model.

In this paper, we describe methods for building and evaluation of limited domain question-answering characters. Several classification techniques are tested, including text classification using support vector machines, language-model based retrieval, and cross-language information retrieval techniques, with the latter having the highest success rate. We also evaluated the effect of speech recognition errors on performance with users, finding that retrieval is robust until recognition reaches over 50 % WER. 1