POLIBITS, Vol. 60, pp. 73-81, 2019.
Abstract: Topic detection, zero-shot learning, NLP, deep learning
Keywords: We present a method to detect topics in news articles. The topics of interest are each represented by a descriptive document. We train a model that can be seen as a similarity function between such a descriptive document and a news article. Our model is a neural network that operates on two kinds of inputs. (1) The full texts of the descriptive documents and the news articles are passed through the same recurrent encoder network and then the distance of the resulting encodings is taken. (2) Our proprietary NLP pipeline and knowledge base are used to recognize named entities and significant keywords and we compute features based on their overlap for a descriptive document and a news article. Our model finally combines the encoding distance with the overlap features and acts as a binary classifier. We evaluate and compare several model configurations on two datasets, a large one automatically created from Wikipedia and a smaller one created manually.
PDF: Zero-Shot Learning for Topic Detection in News Articles
PDF: Zero-Shot Learning for Topic Detection in News Articles
https://doi.org/10.17562/PB-60-9
Table of contents of POLIBITS 60