  Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based
on propositional logic, which has demonstrated competitive performance in many
Natural Language Processing (NLP) tasks, including sentiment analysis, text
classification, and Word Sense Disambiguation. 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 performance and interpretability of TM. 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.
