Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
This work addresses the need for word-level interpretability in novelty detection for applications requiring fine-grained analysis, though it is incremental as it builds on existing Tsetlin machine techniques.
The authors tackled the problem of explaining novelty detection in text by proposing a Tsetlin machine-based method to score individual words for their contribution to novelty, breaking down novelty into interpretable phrases.
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.