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.
@article{arxiv.2105.04708,
title = {Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines},
author = {Bimal Bhattarai and Ole-Christoffer Granmo and Lei Jiao},
journal= {arXiv preprint arXiv:2105.04708},
year = {2021}
}