Unsupervised topic modeling for short texts
AssigneeAT&T Intellectual Property I, L.P.
InventorVivek Kumar Rangarajan Sridhar
Life of this patent
Estimating the term…
Abstract
Topics are determined for short text messages using an unsupervised topic model. In a training corpus created from a number of short text messages, a vocabulary of words is identified, and for each word a distributed vector representation is obtained by processing windows of the corpus having a fixed length. The corpus is modeled as a Gaussian mixture model in which Gaussian components represent topics. To determine a topic of a sample short text message, a posterior distribution over the corpus topics is obtained using the Gaussian mixture model.
Claims20
A method for determining a topic of a sample short text message, comprising:
- by a computer, identifying a vocabulary of words in a corpus, the corpus comprising a plurality of training short text messages;
- by the computer, obtaining distributed vector representations of the words in the vocabulary by processing windows of the corpus having a fixed length;
- by the computer, estimating a plurality of Gaussian components of a Gaussian mixture model of the corpus using the distributed vector representations, the Gaussian components representing corpus topics;
- by the computer, receiving a sample short text message comprising words in the vocabulary;
- and
- by the computer, determining the topic of the sample short text message based on a posterior distribution over the corpus topics for the sample short text message, the posterior distribution obtained using the Gaussian mixture model.
A message topic trend alert system of a communications network, comprising:
- at least one interface to the communications network configured for receiving short text messages transmitted within the short message communications network;
- at least one processor;
- and
- at least one computer readable storage device having stored thereon computer readable instructions that, when executed by the at least one processor, cause the at least one processor to perform operations for generating an alert based on a message topic trend, comprising:
- identifying a vocabulary of words in a corpus, the corpus comprising a plurality of training short text messages;
- obtaining distributed vector representations of the words in the vocabulary by processing windows of the corpus having a fixed length;
- estimating a plurality of Gaussian components of a Gaussian mixture model of the corpus using the distributed vector representations, the Gaussian components representing corpus topics;
- receiving a plurality of sample short text messages comprising words in the vocabulary;
- determining topics of the sample short text messages based on a posterior distribution over the corpus topics for the sample short text messages, the posterior distribution obtained using the Gaussian mixture model;
- identifying a trend in topics of the short text messages;
- and
- generating an alert based on the trend.
A tangible computer-readable medium having stored thereon computer readable instructions for determining a topic of a sample short text message, wherein execution of the computer readable instructions by a processor causes the processor to perform operations comprising:
- identifying a vocabulary of words in a corpus, the corpus comprising a plurality of training short text messages;
- obtaining distributed vector representations of the words in the vocabulary by processing windows of the corpus having a fixed length;
- estimating a plurality of Gaussian components of a Gaussian mixture model of the corpus using the distributed vector representations, the Gaussian components representing corpus topics;
- receiving a sample short text message comprising words in the vocabulary;
- and
- determining the topic of the sample short text message based on a posterior distribution over the corpus topics for the sample short text message, the posterior distribution obtained using the Gaussian mixture model.
Dependencies are inferred from the claim text.