Deep structured semantic model produced using click-through data

A method implemented by one or more computing devices the method comprising:

• receiving a first input item which represents first linguistic information provided in a vocabulary space having a first dimensionality;
• converting the first input item into a phonetic representation of the first input item;
• projecting, using a model, the phonetic representation of the first input item into a first output item, the first output item being expressed in a semantic space;
• receiving a second output item which represents second linguistic information, the second output item also being expressed in the semantic space;
• and
• determining a similarity between the first output item and the second output item in the semantic space to obtain a similarity measure between the first linguistic information and the second linguistic information,
• said projecting using a deep neural network, and
• the model being discriminatively trained based on click-through data.

A system comprising:

• a processing device;
• and
• a computer readable storage medium storing instructions which, when executed by the processing device, cause the processing device to:
• receive an input item that represents linguistic information comprising an input word from a vocabulary space having a first dimensionality;
• represent the input item as a plurality of n-grams;
• map the input into a lower-dimension item that represents the plurality of n-grams in another space having a second dimensionality that is smaller than the first dimensionality of the vocabulary space of the linguistic information;
• and
• use a model to project the lower-dimension item that represents the plurality of n-grams into a semantic space to obtain a semantic output item representing the input item,
• wherein the model is trained using click-through data.