Semantic clustering and user interfaces
First Claim
1. A method implemented by one or more computer processors configured to perform semantic clustering, the method comprising:
- comparing a first set of semantic clusters, formed from a first corpus of user utterances using a computational linguistic analysis, with a second set of semantic clusters formed from a second corpus of user utterances using a computational linguistic analysis;
identifying semantic clusters having topics that are common to the first and second sets of corpora of user utterances; and
outputting a result of the identifying of semantic clusters having topics that are common to the first and second sets of corpora of user utterances.
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Abstract
Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.
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Citations
35 Claims
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1. A method implemented by one or more computer processors configured to perform semantic clustering, the method comprising:
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comparing a first set of semantic clusters, formed from a first corpus of user utterances using a computational linguistic analysis, with a second set of semantic clusters formed from a second corpus of user utterances using a computational linguistic analysis; identifying semantic clusters having topics that are common to the first and second sets of corpora of user utterances; and outputting a result of the identifying of semantic clusters having topics that are common to the first and second sets of corpora of user utterances. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method implemented by one or more computer processors configured to perform semantic clustering, the method comprising:
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processing a corpus by computationally clustering a plurality of semantic graphs of user utterances formed form the corpus such that each said cluster corresponds to similar topics; and outputting a result of the processing in a user interface that indicates prominence of one or more said topics in the corpus. - View Dependent Claims (21, 22, 23, 24, 25)
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26. A method implemented by one or more computer processors configured to perform semantic clustering, the method comprising:
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clustering a plurality of semantic graphs that were formed based on a computational linguistic analysis of a corpus of user utterances into a plurality of semantic clusters through use of one or more proximity metrics; and calculating to which of the plurality of clusters at least one utterance is to be attached. - View Dependent Claims (27, 28, 29, 30, 31)
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32. A method implemented by one or more computer processors configured to perform semantic clustering, the method comprising:
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clustering a plurality of semantic graphs that were formed based on a computational linguistic analysis of a corpus of user utterances into a plurality of semantic clusters through use of one or more proximity metrics; and correlating analytics data with one or more of the semantic clusters of semantic graphs formed based on a computational linguistic analysis of a corpus of user utterances. - View Dependent Claims (33, 34, 35)
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Specification