Probabilistic retrospective event detection
First Claim
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1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising:
- initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events;
probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises;
estimating parameters for the generative model using the event parameters;
generating event clusters to cluster events reported by the documents using estimated generative model parameters;
for each event cluster;
increasing or decreasing a number of events to represent a corresponding event;
if the number of events is not a minimum or a maximum number of events;
(a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and
(b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; and
if the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events;
selecting the first event reported by one or more of the documents; and
for each entity associated with the first event;
generating a respective news article for the first event; and
determining a time for the respective news article.
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Abstract
Probabilistic retrospective event detection is described. In one aspect, event parameters are initialized to identify a number of events from a corpus of documents. Using a generative model, documents are determined to be associated with an event to detect representative events from the identified number of events.
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Citations
17 Claims
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1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising:
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initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events; probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises; estimating parameters for the generative model using the event parameters; generating event clusters to cluster events reported by the documents using estimated generative model parameters; for each event cluster; increasing or decreasing a number of events to represent a corresponding event; if the number of events is not a minimum or a maximum number of events;
(a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and
(b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; andif the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events; selecting the first event reported by one or more of the documents; and for each entity associated with the first event; generating a respective news article for the first event; and determining a time for the respective news article. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising:
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initializing event parameters to identify a salient number of events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents, each event having an immediately preceding event in time and an immediately succeeding event in time, wherein the salient events are identified based on a salient score calculated by the amount of time between the immediately preceding event and the immediately succeeding event for each respective event; estimating parameters for a generative model for probabilistic retrospective detection of the events from the salient number of events, the generative model comprising respective models for person(s), time(s), location(s), and keyword(s); clustering events represented by documents using the parameters for the generative model; increasing or decreasing a number of events associated with respective ones of clustered events to re-initialize events; for respective event clusters, if a minimum or maximum number of events has not been reached, again performing operations of the estimating, clustering, and increasing or decreasing; and for respective event clusters, if a minimum or maximum number of events has been reached, summarizing events in resulting event clusters. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A system comprising:
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a processor; memory; and a retrospective event detection module stored in the memory and executable on the processor, wherein when the retrospective event detection module is executed the processor is configured to perform acts comprising; setting event parameters to identify documents comprising respective events; probabilistically detecting events from documents using a multi-modal generative model, the generative model comprising independent mixture models to model document content associated with an event and time associated with the event, the document content comprising information corresponding to one or more of persons, locations, and keywords; and selecting the event reported by one or more of the documents; and for each entity associated with the event; generating a respective news article for the event; and determining a time for the respective news article; wherein the processor iteratively implements operations for setting the event parameters and probabilistically detecting the events until a configurable minimum or maximum number of events associated with respective ones of one or more salient events has been detected, each event having an immediately preceding event in time and an immediately succeeding event in time, wherein the salient events are identified based on a salient score calculated by the amount of time between the immediately preceding event and the immediately succeeding event for each respective event.
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Specification