Events are an integral part of our day-to-day search needs. Users search for various kinds of events such as political events, organizational announcements, policy changes, personal events, criminal activity and so on. In linguistics, events are often thought of as discourse entities with associated complex structure and attributes.

This paper introduces the coreferent mention retrieval task, in which the goal is to retrieve sentences that mention a specific entity based on a query by example in which one sentence mentioning that entity is provided. The development of a coreferent mention retrieval test collection is then described. Results are presented for five coreferent mention retrieval systems, both to illustrate the use of the collection and to specify the results that were pooled on which human coreference judgments were performed. The new test collection is built from content that is available from the Linguistic Data Consortium; the partitioning and human annotations used to create the test collection atop that content are being made freely available.

Typical search engines for spoken content begin with some form of language-specific audio processing such as phonetic word recognition. Many languages, however, lack the language tuned preprocessing tools that are needed to create indexing terms for speech. One approach in such cases is to rely on repetition, detected using acoustic features, to find terms that might be worth indexing. Experiments have shown that this approach yields term sets that might be sufficient for some applications in both spoken term detection and ranked retrieval experiments. Such approaches currently work only with spoken queries, however, and only when the searcher is able to speak in a manner similar to that of the speakers in the collection. This demonstration paper proposes Vapor Engine, a new tool for selectively transcribing repeated terms that can be automatically detected from spoken content in any language. These transcribed terms could then be matched to queries formulated using written terms. Vapor Engine is early in development: it currently supports only single-term queries and has not yet having been formally evaluated. This paper introduces the interface and summarizes the challenges it seeks to address.

Jerome White, Douglas Oard, Aren Jansen, Jiaul Paik, Rashmi Sankepally. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

The development of a new test collection is described in which the task is to search naturally occurring spoken content using naturally occurring spoken queries. To support research on speech retrieval for low-resource settings, the collection includes terms learned by zero-resource term discovery techniques. Use of a new tool designed for exploration of spoken collections provides some additional insight into characteristics of the collection.