Unsupervised Key-phrase Extraction and Clustering for Classification Scheme in Scientific Publications

• URL: http://arxiv.org/abs/2101.09990v2
• Date: Mon, 8 Feb 2021 20:31:42 GMT
• Title: Unsupervised Key-phrase Extraction and Clustering for Classification Scheme in Scientific Publications
• Authors: Xiajing Li, Marios Daoutis
• Abstract summary: We investigate possible ways of automating parts of the Systematic Mapping (SM) and Systematic Review (SR) process. Key-phrases are extracted from scientific documents using unsupervised methods, which are then used to construct the corresponding Classification Scheme. We also explore how clustering can be used to group related key-phrases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several methods have been explored for automating parts of Systematic Mapping (SM) and Systematic Review (SR) methodologies. Challenges typically evolve around the gaps in semantic understanding of text, as well as lack of domain and background knowledge necessary to bridge that gap. In this paper we investigate possible ways of automating parts of the SM/SR process, i.e. that of extracting keywords and key-phrases from scientific documents using unsupervised methods, which are then used as a basis to construct the corresponding Classification Scheme using semantic key-phrase clustering techniques. Specifically, we explore the effect of ensemble scores measure in key-phrase extraction, we explore semantic network based word embedding in embedding representation of phrase semantics and finally we also explore how clustering can be used to group related key-phrases. The evaluation is conducted on a dataset of publications pertaining the domain of "Explainable AI" which we constructed using standard publicly available digital libraries and sets of indexing terms (keywords). Results shows that: ensemble ranking score does improve the key-phrase extraction performance. Semantic-network based word embedding based on the ConceptNet Semantic Network has similar performance with contextualized word embedding, however the former are computationally more efficient. Finally Semantic key-phrase clustering at term-level can group similar terms together that can be suitable for classification scheme.

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