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Improving Explainability and Accuracy through Feature Engineering: A Taxonomy of Features in NLP-based Machine Learning

2021 | International Conference on Information Systems | Citations: 0
Authors: Wambsganss, Thiemo; Engel, Christian; Fromm, Hansjörg

Abstract: Natural Language Processing (NLP)-based machine learning receives continuous at ... Expand

Abstract: Natural Language Processing (NLP)-based machine learning receives continuous attention in Information System (IS) research and practice. Despite the success of deep learning models, NLP feature engineering still plays a vital role in contexts where only little annotated data is available, and in which explainability is a precondition for productive deployment. However, NLP feature engineering is a labor-intensive and timeconsuming endeavor, and there is still limited shared knowledge about the distinctive characteristics of NLP features from an interdisciplinary perspective. To address this gap, we draw on a systematic literature review and develop a five-dimensional NLP feature taxonomy based on 133 unique features from 211 scientific studies. This helps IS researchers and practitioners to classify, compare, and evaluate their NLP studies. Moreover, we used cluster heat mapping analysis to derive three clusters and several white spots to provide further assistance for designing new NLP solutions in IS. Collapse

Semantic filters: feature engineering linguistic analysis

Topics: database system information retrieval deception detection fake news Python

Methods: natural language processing feature engineering structured literature research cluster analysis qualitative interview
TOWARDS A TAXONOMY OF TEXT MINING FEATURES

2019 | European Conference On Information Systems | Citations: 0
Authors: Fromm, Hansjörg; Wambsganss, Thiemo; Söllner, Matthias

Abstract: Recently, text mining has received special attention from both researchers and p ... Expand

Abstract: Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and information sciences. However, text mining features that evolved in one particular discipline are often unknown or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our taxonomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to provide a deeper understanding. Collapse

Semantic filters: feature engineering linguistic analysis

Topics: information retrieval database system

Methods: natural language processing feature engineering term frequency–inverse document frequency linguistic analysis personal interview

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Improving Explainability and Accuracy through Feature Engineering: A Taxonomy of Features in NLP-based Machine Learning

TOWARDS A TAXONOMY OF TEXT MINING FEATURES