  Recent research in novelty detection focuses mainly on document-level
classification, employing deep neural networks (DNN). However, the black-box
nature of DNNs makes it difficult to extract an exact explanation of why a
document is considered novel. In addition, dealing with novelty at the
word-level is crucial to provide a more fine-grained analysis than what is
available at the document level. In this work, we propose a Tsetlin machine
(TM)-based architecture for scoring individual words according to their
contribution to novelty. Our approach encodes a description of the novel
documents using the linguistic patterns captured by TM clauses. We then adopt
this description to measure how much a word contributes to making documents
novel. Our experimental results demonstrate how our approach breaks down
novelty into interpretable phrases, successfully measuring novelty.
