A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Re-Ranking Words to Improve Interpretability of Automatically Generated Topics
2019
Proceedings of the 13th International Conference on Computational Semantics - Long Papers
orcid.org/0000-0002-9483-6006 (2019) Re-ranking words to improve interpretability of automatically generated topics. Abstract Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models. Conventionally, topics are represented by their n most probable words, however,
doi:10.18653/v1/w19-0404
dblp:conf/iwcs/AlokailiAS19
fatcat:lewikfpkhvgzrc2x65y34qh5la