This paper presents GnuTutor, an open source intelligent tutoring system (ITS) inspired by the AutoTutor ITS. The goal of GnuTutor is to create a freely available, open source ITS platform that can be used by schools and researchers alike. To achieve this goal, significant departures from AutoTutor's current design were made so that GnuTutor would use a smaller, non-proprietary code base but have the major functionality of AutoTutor, including mixed-initiative dialogue, an animated agent, speech act classification, and natural language understanding using latent semantic analysis. This paper describes the GnuTutor system, its components, and the major differences between GnuTutor and AutoTutor.

We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.

We then describe In this paper, we describe a user evaluation of a humanrobot a study which assessed the responses of naïve users dialogue system that is designed to enable a humanoid to output that varied along two dimensions: the robot to cooperate with a human partner on building wooden method of describing an assembly plan (pre-order construction toys. In the evaluation, we experimentally vary or post-order), and the method of referring to objects two aspects of the output generated by the system: the way in the world (basic and full). Varying both that it describes assembly plans to the user, and the way that of these factors produced significant results: subjects it refers to objects in the world. We then measure the impact using the system that employed a pre-order of varying each of these features on the users' objective success description strategy asked for instructions to be repeated at working with the system, as well as on their subjective significantly less often than those who experienced impressions of the interaction.

Dialogue-based Question Answering (QA) is a highly complex task that brings together a QA system including various natural language processing components (i.e., components for question classification, information extraction, and retrieval) with dialogue systems for effective and natural communication. The dialogue-based access is difficult to establish when the QA system in use is complex and combines many different answer services with different quality and access characteristics. For example, some questions are processed by opendomain QA services with a broad coverage. Others should be processed by using a domain-specific instance ontology for more reliable answers. Different answer services may change their characteristics over time and the dialogue reaction models have to be updated according to that. To solve this problem, we developed introspective methods to integrate adaptable models of the answer services. We evaluated the impact of the learned models on the dialogue performance, i.e., whether the adaptable models can be used for a more convenient dialogue formulation process. We show significant effectiveness improvements in the resulting dialogues when using the machine learning (ML) models. Examples are provided in the context of the generation of system-initiative feedback to user questions and answers, as provided by heterogeneous information services.

An exception is which tracks the extent to which material has our Degrees of Grounding model [Roque and Traum, 2008], reached mutual belief in a dialogue, and conduct which provides a more detailed description of the extent to experiments in which the model is used to manage which material has become a part of the common ground during grounding behavior in spoken dialogues with a virtual a dialogue. In this paper we describe experiments in applying human. We show that the model produces improvements that model to handle explicit grounding behavior in in virtual human performance as measured a virtual human. We begin by describing the model and the by post-session questionnaires.

We propose a new topic model for tracking time-varying consumer purchase behavior, in which consumer interests and item trends change over time. The proposed model can adaptively track changes in interests and trends based on current purchase logs and previously estimated interests and trends. The online nature of the proposed method means we do not need to store past data for current inferences and so we can considerably reduce the computational cost and the memory requirement. We use real purchase logs to demonstrate the effectiveness of the proposed method in terms of the prediction accuracy of purchase behavior and the computational cost of the inference.

In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective.

Clustering and visualization of large text document collections aids in browsing, navigation, and information retrieval. We present a document clustering and visualization method based on Latent Dirichlet Allocation and self-organizing maps (LDA-SOM). LDA-SOM clusters documents based on topical content and renders clusters in an intuitive two-dimensional format. Document topics are inferred using a probabilistic topic model. Then, due to the topology preserving properties of self-organizing maps, document clusters with similar topic distributions are placed near one another in the visualization. This provides the user an intuitive means of browsing from one cluster to another based on topics held in common. The effectiveness of LDA-SOM is evaluated on the 20 Newsgroups and NIPS data sets.

This year's conferences were held in Perhaps one of the true litmus tests of any conference is the caliber of the invited speakers. Sensibility: Sentiment Analysis, Opinion and research manager at Microsoft Research) The distinguished Robert S. Englemore Mining, and the Computational who gave his AAAI presidential Memorial Award Lecture was delivered Treatment of Subjective Language"), address, "Artificial Intelligence in the by Kenneth Ford (Florida Institute while Seth C. Goldstein (Carnegie Open World." Mel lon University) discussed revolutionary Chris Urmson (Carnegie Mellon In his lecture, "Toward Cognitive work in self-reconfiguring programmable University), a leading member of the Prostheses," Ford discussed human-centered matter composed of ensembles of submillimeter robots in his DARPA Urban Grand Challenge winning computing to amplify talk, "Realizing Claytronics: A Challenge team, described the race and winning human cognition and perception. Instead of the learning for network analysis in ("From Images to Scenes: Using popular competition, which has his talk, "Making Sense of Complex Lots of Data to Infer Geometric, Photometric, pushed the envelope of mobile robotics Networks." David Haussler (University and Semantic Scene Properties since its inception, this year was of California, Santa Cruz) traced the from a Single Image"), and Lillian host to a Robot Workshop and Exhibition.

We investigate the problem of learning a widely-used latent-variable model - the Latent Dirichlet Allocation (LDA) or "topic" model - using distributed computation, where each of