Detecting and Browsing Events in Unstructured text

Abstract

Previews and overviews of large, heterogeneous information resources help users comprehend the scope of collections and focus on particular subsets of interest. For narrative documents, questions of "what happened? where? and when?" are natural points of entry. Building on our earlier work at the Perseus Project with detecting terms, place names, and dates, we have exploited co-occurrences of dates and place names to detect and describe likely events in document collections. We compare statistical measures for determining the relative significance of various events. We have built interfaces that help users preview likely regions of interest for a given range of space and time by plotting the distribution and relevance of various collocations. Users can also control the amount of collocation information in each view. Once particular collocations are selected, the system can identify key phrases associated with each possible event to organize browsing of the documents themselves.

Generic method for detecting focus time of documents

“…Their method extracted dates from sentences, ranked them, and then chronologically ordered. Smith [42] measured the co-occurrences of dates and place names in historical corpora to detect the events mentioned in document collections and analyzed how various interest measures (raw counts, chi-square, log likelihood, etc.) performed for ranking rare events.…”

New Formats and Interfaces for Multi-Document News Summarization and its Evaluation

“…Based on the differences in their story representations, we distinguish between three types of TTM tracking methods: (a) keyword representation, (b) group representation, and (c) combo representation methods. Type (a) methods use a list of bursty n-grams ranked by their burst scores (Kleinberg, 2002;Fung, Yu, Yu, & Lu, 2005;Gruhl, Guha, Kumar, Novak, & Tomkins, 2005;He, Chang, Lim, & Zhang, 2007;Smith, 2002). Type (b) methods assemble bursty n-grams into groups which point to subjects (Fung et al, 2005;Wang & McCallum, 2006;Mei & Zhai, 2005;Schult & Spiliopoulou, 2006;Janssens, Glänzel, & Moor, 2007).…”

Monitoring Opinions in Online Forums – A Case Study from the Sports Industry

“…Several researchers address the task of detecting events in texts. They introduce methods for identifying new events in unstructured texts [11], propose techniques for browsing document collections in order to detect events [16] and present ways of finding novel events in a temporarily ordered stream of news stories [19]. Some papers analyze correlations between online communication and stock movements.…”