Related papers: Ontology-based Interpretable Machine Learning for Textual Data

Ontology-based Interpretable Machine Learning for Textual Data

URL: http://arxiv.org/abs/2004.00204v1
Date: Wed, 1 Apr 2020 02:51:57 GMT
Title: Ontology-based Interpretable Machine Learning for Textual Data
Authors: Phung Lai, NhatHai Phan, Han Hu, Anuja Badeti, David Newman, Dejing Dou
Abstract summary: We introduce a novel interpreting framework that learns an interpretable model based on sampling technique to explain prediction models. To narrow down the search space for explanations, we design a learnable anchor algorithm. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible explanations.
Score: 35.01650633374998
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.

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