# Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

arXiv: https://arxiv.org/abs/2011.08755 · HTML: https://mlsanity.com/paper/2011.08755/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)
- [Coalesced Multi-Output Tsetlin Machines with Clause Sharing](https://mlsanity.com/paper/2108.07594/) (2021)
- [On the Convergence of Tsetlin Machines for the XOR Operator](https://mlsanity.com/paper/2101.02547/) (2021)
- [Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size](https://mlsanity.com/paper/2301.08190/) (2023)
- [Enhancing Interpretable Clauses Semantically using Pretrained Word Representation](https://mlsanity.com/paper/2104.06901/) (2021)
- [Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines](https://mlsanity.com/paper/2105.04708/) (2021)
- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites](https://mlsanity.com/paper/2406.00704/) (2024)
- [On the Convergence of Tsetlin Machines for the AND and the OR Operators](https://mlsanity.com/paper/2109.09488/) (2021)
- [Self-timed Reinforcement Learning using Tsetlin Machine](https://mlsanity.com/paper/2109.00846/) (2021)
- [An FPGA Architecture for Online Learning using the Tsetlin Machine](https://mlsanity.com/paper/2306.01027/) (2023)
- [Contracting Tsetlin Machine with Absorbing Automata](https://mlsanity.com/paper/2310.11481/) (2023)
- [The Sparse Tsetlin Machine: Sparse Representation with Active Literals](https://mlsanity.com/paper/2405.02375/) (2024)
- [Pruning Literals for Highly Efficient Explainability at Word Level](https://mlsanity.com/paper/2411.04557/) (2024)
- [Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference](https://mlsanity.com/paper/2607.20124/) (2026)

## Abstract (arXiv)

Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses interpretable open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine (TM) with a novelty scoring mechanism. The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data. We demonstrate that the clauses provide a succinct interpretable description of known topics, and that our scoring mechanism makes it possible to discern novel topics from the known ones. Empirically, our TM-based approach outperforms seven other novelty detection schemes on three out of five datasets, and performs second and third best on the remaining, with the added benefit of an interpretable propositional logic-based representation.