TOPIC MODELS

摘要

Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.

主权项

1. A method for predicting a topic of a document, comprising:
processing a document representation of a document and features of the document using a topic model, the processing comprising:
determining a feature/topic prediction for a feature of the document, the feature/topic prediction specifying a probability of the feature being indicative of a first topic;determining a word/topic prediction for a word within the document, the word/topic prediction specifying a probability of the word being indicative of a second topic; anddetermining a document/topic prediction for the document based upon the feature/topic prediction and the word/topic prediction.