6533b825fe1ef96bd1283253
RESEARCH PRODUCT
Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines
Bimal BhattaraiOle-christoffer GranmoLei Jiaosubject
FOS: Computer and information sciencesI.2Computer Science - Machine LearningArtificial Intelligence (cs.AI)Computer Science - Computation and LanguageI.5Artificial IntelligenceComputer Science - Artificial IntelligenceI.2; I.5; I.7Computation and Language (cs.CL)I.7VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Machine Learning (cs.LG)description
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-05-10 |