Topic classification using a jointly trained artificial neural network

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• access an input vector representing an input post, wherein:
• the input post comprises one or more n-grams and an image;
• the input vector corresponds to a point in a d-dimensional vector space;
• the input vector was generated by an artificial neural network (ANN) based on a text vector representing the one or more n-grams of the input post and an image vector representing the image of the input post;
• and
• the ANN was jointly trained to receive a text vector representing one or more n-grams of a post and an image vector representing an image of the post and then output a probability that the received post is related to the training posts of a training page;
• and
• determine a topic of the input post based on the input vector.

A system comprising:

• one or more processors at a first client computing device;
• and
• a memory at the first client computing device coupled to the processors and comprising instructions operable when executed by the processors to cause the processors to:
• access an input vector representing an input post, wherein:
• the input post comprises one or more n-grams and an image;
• the input vector corresponds to a point in a d-dimensional vector space;
• the input vector was generated by an artificial neural network (ANN) based on a text vector representing the one or more n-grams of the input post and an image vector representing the image of the input post;
• and
• the ANN was jointly trained to receive a text vector representing one or more n-grams of a post and an image vector representing an image of the post and then output a probability that the received post is related to the training posts of a training page;
• and
• determine a topic of the input post based on the input vector.