Systems and methods for detecting and coordinating changes in lexical items
First Claim
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1. A method for detecting and coordinating change events in a data stream comprising:
- receiving the data stream, the data stream comprising a plurality of lexical items and a metavalue associated therewith;
monitoring a probability of occurrence of the lexical items in the data stream over time according to a lexical occurrence model to detect a plurality of change events in the data stream;
applying a significance test to the change events to determine if the change events are statistically significant;
applying an interestingness test to determine if the change events are likely to be of interest to a user, the interestingness test defined using conditional mutual information between the lexical items and the lexical occurrence model given a time span as provided by the relationship;
I(W;
M|T)⇄
H(W|T)−
H(W|M,T)to determine the amount of information that is derived from the change events;
grouping the change events across the lexical items and the metavalue to summarize the change events that are synchronous in time, the grouping forming a set of grouped change events; and
presenting, via an output device, a summarization of the grouped change events to the user.
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Abstract
Systems and methods for efficiently detecting and coordinating step changes, trends, cycles, and bursts affecting lexical items within data streams are provided. Data streams can be sourced from documents that can optionally be labeled with metadata. Changes can be grouped across lexical and/or metavalue vocabularies to summarize the changes that are synchronous in time. The methods described herein can be applied either retrospectively to a corpus of data or in a streaming mode.
6 Citations
24 Claims
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1. A method for detecting and coordinating change events in a data stream comprising:
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receiving the data stream, the data stream comprising a plurality of lexical items and a metavalue associated therewith; monitoring a probability of occurrence of the lexical items in the data stream over time according to a lexical occurrence model to detect a plurality of change events in the data stream; applying a significance test to the change events to determine if the change events are statistically significant; applying an interestingness test to determine if the change events are likely to be of interest to a user, the interestingness test defined using conditional mutual information between the lexical items and the lexical occurrence model given a time span as provided by the relationship;
I(W;
M|T)⇄
H(W|T)−
H(W|M,T)to determine the amount of information that is derived from the change events; grouping the change events across the lexical items and the metavalue to summarize the change events that are synchronous in time, the grouping forming a set of grouped change events; and presenting, via an output device, a summarization of the grouped change events to the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer readable medium comprising computer readable instructions that, when executed, perform the steps of:
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receiving a data stream, the data stream comprising a plurality of lexical items and a metavalue associated therewith; monitoring a probability of occurrence of the lexical items in the data stream over time according to a lexical occurrence model to detect a plurality of change events in the data stream; applying a significance test to the change events to determine if the change events are statistically significant; applying an interestingness test to determine if the change events are likely to be of interest to a user, the interestingness test defined using conditional mutual information between the lexical items and the lexical occurrence model given a time span as provided by the relationship;
I(W;
M|T)=H(W|T)−
H(W|M,T)to determine an amount of information that is derived from the change events; grouping the change events across the lexical items and the metavalue to summarize the change events that are synchronous in time, the grouping forming a set of grouped change events; and presenting, via an output device, a summarization of the grouped change events to the user. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computing system for detecting and coordinating change events in a data stream, comprising:
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a processor; an output device; and a memory in communication with the processor, the memory being configured to store instructions, executable by the processor to; receive a data stream, the data stream comprising a plurality of lexical items and a metavalue associated therewith; monitor a probability of occurrence of the lexical items in the data stream over time according to a lexical occurrence model to detect a plurality of change events in the data stream; apply a significance test to the change events to determine if the change events are statistically significant; apply an interestingness test to determine if the change events are likely to be of interest to a user, the interestingness test defined using conditional mutual information between the lexical items and the lexical occurrence model given a time span as provided by the relationship;
I(W;
M|T)=H(W|T)−
H(W|M,T)to determine an amount of information that is derived from the change events; group the change events across the lexical items and the metavalue to summarize the change events that are synchronous in time, the grouping forming a set of grouped change events; and present, via the output device, a summarization of the grouped change events to the user. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
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Specification