Probabilistic retrospective event detection

• US 7,788,263 B2
• Filed: 10/21/2005
• Issued: 08/31/2010
• Est. Priority Date: 08/10/2005
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ma, Wei-Ying, Li, Mingjing, Li, Zhiwei
• Primary Examiner(s): Trujillo; James
• Assistant Examiner(s): CONYERS, DAWAUNE A
• Application Number:
  US11/256,353
• Publication Number:
  US 20070038653A1
• Time in Patent Office: 1,775 Days
• Field of Search: 707/3, 707100-1041, 707200-206, 707/738
• US Class Current: 707/738
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/3346 using probabilistic model

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.

Claims

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.

2. The method of claim 1, wherein the generative model determines probabilistic distributions of persons, locations, keywords, and time conditioned on events to cluster document(s) to events.

3. The method of claim 1, wherein the generative model models document content and timestamps associated with events with different Bayesian mixture models.

4. The method of claim 1, wherein operations associated with the selecting, generating, and determining use one or more of mixing proportions of events, priors of events, conditional multinomial distributions given the event, and parameters of a conditional Gaussian distribution given the event.

5. The method of claim 1, wherein document content associated with an event of the respective events comprises one or more of information associated with persons, locations, and keywords and wherein probabilistically determining further comprises:

modeling the document content with mixtures of different respective unigram models; and

modeling the timestamps with a mixture model that is not one of the different respective unigram models.

6. The method of claim 1, wherein estimating the parameters further comprises estimating model parameters using expectation maximization to iteratively maximize log-likelihood.

7. The method of claim 1, wherein generating the event clusters further comprises:

representing probability associated with time as a function of whether a date of the document is associated with a beginning stage of the event or associated with an end stage of the event; and

wherein the beginning stage and the end stage represent a continuous duration of the event.

8. The method of claim 1, wherein generating the event clusters further comprises:

independently estimating, for respective person, location, and keyword models, model parameters by iteratively determining expectation and maximization probabilities, the model parameters being a number of documents and a number of events; and

calculating the probability using the models in view of temporal continuity of the event and any overlap of two or more of the events.

9. 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 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.

10. The method of claim 9, wherein initializing the event parameters further comprises:

selecting an event reported by one or more 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.

11. The method of claim 10, wherein operations associated with the selecting, generating, and determining use one or more of mixing proportions of events, priors of events, conditional multinomial distributions given the event, and parameters of a conditional Gaussian distribution given the event.

12. The method of claim 9, wherein the generative model models document content and timestamps associated with events with different Bayesian models.

13. The method of claim 9, wherein the generative model models document content and timestamps associated with events with different Bayesian models, and wherein the document content comprises one or more of information associated with persons, locations, and keywords and wherein estimating the parameters further comprises:

modeling the persons, locations, and keywords with mixtures of different respective unigram models.

14. The method of claim 9, wherein estimating the parameters further comprises modeling time with a mixture model that is different than respective mixture models used to model document content associated with an event.

15. The method of claim 9, wherein estimating the parameters further comprises estimating model parameters using expectation maximization to iteratively maximize log-likelihood.

16. The method of claim 9, wherein generating the event clusters further comprises:

independently estimating, for respective person, location, and keyword models, model parameters by iteratively determining expectation and maximization probabilities, the model parameters being a number of documents and a number of events; and

calculating the probability using the models in view of temporal continuity of the event and any overlap of two or more of the events.

17. A system comprising:

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.