An implementation of the paper: Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
This is an implementation of a latent semantic model that intend to map a query to its relevant document where keyword-based approaches often fails. This is a model with deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them.
This model can be used as a search engine that helps people find out their desired document even with searching a query that:
- has different words than the document
- is abbreviation of the document words
- changed the order of the words in the document
- shortened words in the document
- has typos
- has spacing issues
...
Pythonversion 2.7 or higherNumpyversion 1.13.1 or higherTensorflowversion 1.1.0 or higher
Bahare Fatemi
Computer Science Department
The University of British Columbia
201-2366 Main Mall, Vancouver, BC, Canada (V6T 1Z4)