In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; iii. and event analytics.
. One of the most important seminal works in identifying existing and emerging events were the various tasks in (TDT) @cite_34 . The TDT program aimed to search, organize and structure'' broadcast news media from multiple sources. The five tasks laid within the ambit of TDT where topic tracking, link detection, topic detection, first story detection, and story segmentation. The task of topic tracking required to build a system to detect from an evaluation corpus after being trained on a set stories. The link detection task involved answering a boolean query to whether two given are related by a common topic. The topic detection task comprised of declaring new from incoming which had not been presented to the system. First story detection was another boolean decision task of determining whether the given is a seed story (first-story) to create a new cluster. Story segmentation task required segmentation of an incoming stream of text into .
@cite_34
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Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. We establish a novel task in the spirit of news summarization and topic detection and tracking (TDT): daily determination of the topics newly popular with Wikipedia readers. Central to this effort is a new public dataset consisting of the hourly page view statistics of all Wikipedia articles over the last three years. We give baseline results for the tasks of: discovering individual pages of interest, clustering these pages into coherent topics, and extracting the most relevant summarizing sentence for the reader. When compared to human judgements, our system shows the viability of this task, and opens the door to a range of exciting future work. This paper investigates the use and extension of text retrieval and clustering techniques for event detection. The task is to automatically detect novel events from a temporally-ordered stream of news stories, either retrospectively or as the stories arrive. We applied hierarchical and non-hierarchical document clustering algorithms to a corpus of 15,836 stories, focusing on the exploitation of both content and temporal information. We found the resulting cluster hierarchies highly informative for retrospective detection of previously unidentified events, effectively supporting both query-free and query-driven retrieval. We also found that temporal distribution patterns of document clusters provide useful information for improvement in both retrospective detection and on-line detection of novel events. In an evaluation using manually labelled events to judge the system-detected events, we obtained a result of 82 in the Fl measure for retrospective detection, and a Fl value of 42 for on-line detection. Topic Detection and Tracking: Event-based Information Organization brings together in one place state-of-the-art research in Topic Detection and Tracking (TDT). This collection of technical papers from leading researchers in the field not only provides several chapters devoted to the research program and its evaluation paradigm, but also presents the most current research results and describes some of the remaining open challenges. Topic Detection and Tracking: Event-based Information Organization is an excellent reference for researchers and practitioners in a variety of fields related to TDT, including information retrieval, automatic speech recognition, machine learning, and information extraction