This demonstration presents an interface to a corpus of broadcast baseball games that have been indexed using an unsupervised content-based method introduced here. The method uses the concept of a grounded language model to motivate a framework in which video is searched using natural language with no reliance on predetermined concepts or hand labeled events. The interface demonstrates the effectiveness of the technique and the ease of use it affords the user.

Most approaches to highlight classification in the sports domain exploit only limited temporal information. This paper presents a method, called temporal feature induction, which automatically mines complex temporal information from raw video for use in highlight classification. The method exploits techniques from temporal data mining to discover a codebook of temporal patterns that encode long distance dependencies and duration information. Preliminary experiments show that using such induced temporal features significantly improves performance of a baseball highlight classification system.

We introduce a framework for learning situated Natural Language Interfaces (NLIs) to interactive virtual environments. The framework exploits the non-linguistic context, or situation, explicitly modeled in such interactive applications. This situation model is integrated with a model of word meaning in a principled manner using a noisy channel approach to language understanding. Preliminary experimentation in an independently designed interactive application, i.e. the Mission Rehearsal Exercise (MRE), shows that this situated NLI outperforms a state of the art NLI on both whole frame accuracy and F-Score metrics. Further, use of the situation model in the situated NLI is shown to increase robustness to the noise introduced by the use of automatic speech recognition.

Recent work in Question Answering has focused on web-based systems that extract answers using simple lexico-syntactic patterns. We present an alternative strategy in which patterns are used to extract highly precise relational information offline, creating a data repository that is used to efficiently answer questions. We evaluate our strategy on a challenging subset of questions, i.e. "Who is ..." questions, against a state of the art web-based Question Answering system. Results indicate that the extracted relations answer 25% more questions correctly and do so three orders of magnitude faster than the state of the art system.

We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naïve word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial systems.