2000 · 244 citations · 19 references
ComputingComputer ScienceInformation RetrievalNatural Language ProcessingText AnalyticsCorpus AnalysisInformation ExtractionTerm ExtractionTimeline AnalysisTopic ManagementTopic ModelingEvent ExtractionTimeline VisualizationTopic LabelingTopic Tracking
The study proposes a statistical model of feature occurrence over time and develops hypothesis‑testing methods to assess the significance of term appearance on specific dates. The authors use classical hypothesis testing to combine significant terms into topics, then employ these topics to automatically generate interactive timelines of major events, and validate the approach by extracting topics from a test corpus and having human evaluators judge their relevance. Human evaluators rated the automatically generated topics highly, finding them comparable to known topics.
We present a statistical model of feature occurrence over time, and develop tests based on classical hypothesis testing for significance of term appearance on a given date. Using additional classical hypothesis testing we are able to combine these terms to generate “topics” as defined by the Topic Detection and Tracking study. The groupings of terms obtained can be used to automatically generate an interactive timeline displaying the major events and topics covered by the corpus. To test the validity of our technique we extracted a large number of these topics from a test corpus and had human evaluators judge how well the selected features captured the gist of the topics, and how they overlapped with a set of known topics from the corpus. The resulting topics were highly rated by evaluators who compared them to known topics.
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The Analysis of Contingency Tables
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