Convolutional Latent Semantic Models and their Applications

摘要

Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

主权项

1. A method, implemented by one or more computing devices, for processing linguistic items, comprising:
receiving a word sequence formed by a sequence of words; forming a plurality of window vectors, each representing a set of z consecutive words in the word sequence; transforming the window vectors into a plurality of local contextual feature (LCF) vectors, based on a first part of a convolutional latent semantic model; generating a global feature vector by selecting, for each dimension of the LCF vectors, a maximum value specified by the LCF vectors, with respect to that dimension; and projecting the global feature vector into a concept vector, based on a second part of the convolutional latent semantic model, the convolutional latent semantic model being trained based on click-through data.