WS 2019

Re-Ranking Words to Improve Interpretability of Automatically Generated Topics

WS 2019 areejokaili/topic_reranking

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.


Using Multi-Sense Vector Embeddings for Reverse Dictionaries

WS 2019 uds-lsv/Multi-Sense-Embeddings-Reverse-Dictionaries

However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word.

R-grams: Unsupervised Learning of Semantic Units in Natural Language

WS 2019 bakirillov/rgramlib

This paper investigates data-driven segmentation using Re-Pair or Byte Pair Encoding-techniques.