  Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based
on propositional logic. The algorithm has demonstrated competitive performance
in many Natural Language Processing (NLP) tasks, including sentiment analysis,
text classification, and Word Sense Disambiguation (WSD). To obtain human-level
interpretability, legacy TM employs Boolean input features such as bag-of-words
(BOW). However, the BOW representation makes it difficult to use any
pre-trained information, for instance, word2vec and GloVe word representations.
This restriction has constrained the performance of TM compared to deep neural
networks (DNNs) in NLP. To reduce the performance gap, in this paper, we
propose a novel way of using pre-trained word representations for TM. The
approach significantly enhances the TM performance and maintains
interpretability at the same time. We achieve this by extracting semantically
related words from pre-trained word representations as input features to the
TM. Our experiments show that the accuracy of the proposed approach is
significantly higher than the previous BOW-based TM, reaching the level of
DNN-based models.
