Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3041885Utgivelsesdato
2022Metadata
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Originalversjon
Bhattarai, B., Granmo, O.-C. & Lei, J. (2022). Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines. Applied intelligence (Boston), 1-25. doi: 10.1007/s10489-022-03281-1Sammendrag
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 adapt 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.