Survey: Understand the challenges of MachineLearning Experts using Named EntityRecognition Tools
This work addresses the practical problem of tool selection for ML experts working on NER in clinical information retrieval, but is incremental as it applies established survey methodologies to this specific domain.
This survey identifies the criteria ML experts use to evaluate Named Entity Recognition tools and frameworks, and examines the main challenges they face in selecting suitable tools for developing Clinical Practice Guidelines.
This paper presents a survey based on Kasunic's survey research methodology to identify the criteria used by Machine Learning (ML) experts to evaluate Named Entity Recognition (NER) tools and frameworks. Comparison and selection of NER tools and frameworks is a critical step in leveraging NER for Information Retrieval to support the development of Clinical Practice Guidelines. In addition, this study examines the main challenges faced by ML experts when choosing suitable NER tools and frameworks. Using Nunamaker's methodology, the article begins with an introduction to the topic, contextualizes the research, reviews the state-of-the-art in science and technology, and identifies challenges for an expert survey on NER tools and frameworks. This is followed by a description of the survey's design and implementation. The paper concludes with an evaluation of the survey results and the insights gained, ending with a summary and conclusions.