Content Explorer: Recommending Novel Entities for a Document Writer
Abstract
Background research is an inseparable part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research.
Automated tools could help significantly in this discovery process and increase the productivity of the writer.
In this paper, we formulate the problem of recommending topics to a writer.
We formulate this as a supervised learning problem and run a user study to validate this approach.
We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set.
We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.
Automated tools could help significantly in this discovery process and increase the productivity of the writer.
In this paper, we formulate the problem of recommending topics to a writer.
We formulate this as a supervised learning problem and run a user study to validate this approach.
We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set.
We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.
Research Areas
