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Supervised and Unsupervised Text Mining for Grey Literature Screening

2021, Lu, Jinghui, 0000-0001-7149-6961

The increasing recognition of the value of Open Innovation (OI) and the Multi-actor Approach (MAA) in research and innovation activities highlights the need for an efficient and effective process for searching and extracting knowledge from a wide range of different sources, e.g. knowledge is required from academic sources but also from practitioners and intermediaries such as businesses, advisors, policymakers and non-government organisations. While knowledge from academic sources can be relatively easily accessed through peer-reviewed publications, knowledge from other sources may be more widely dispersed. This highlights the potential value of exploring and exploiting grey literature, information produced by organisations where publishing and distributing is not the primary focus, to support research and innovation activities. However, this is not easy given the lack of structure in grey literature, as well as the potentially large amount of irrelevant data that is likely to be included in any grey literature collection. As such, machine-learning-based text mining approaches can be used to facilitate the exploration and exploitation of grey literature, and thus, to enhance research and innovation activities. As one of the most important sectors in Ireland, the agri-food sector underperforms in relation to innovation activities in comparison to other sectors. Therefore, this thesis proposes using text mining approaches to fuel the advance of research and innovation activities in the agri-food sector. There are many challenges in applying text mining approaches to grey literature to support research and innovation activities. In this thesis, we focus on two aspects: using semi-supervised approaches to assist innovation scholars in grey literature screening; using unsupervised corpus comparison to support grey literature content analysis. To semi-automate grey literature screening, we reframe this as a problem of using active learning for grey literature classification. Firstly, we explore the most suitable text representation technique used in active learning, as text representations play an important role in the performance of an active learning system. To this end, we conduct a benchmark experiment comparing the effectiveness of different text representations in the active learning context, especially focusing on more recent high-performing transformer-based text representations. Furthermore, we incorporate the fine-tuning approach into active learning to improve the performance of the transformer-based text representations in active learning. A feature of grey literature compared to other texts is that it is unstructured and often includes long texts, so it is crucial to design a text representation that is suitable for grey literature, and that also works well in the active learning context where labelled data is scarce. Therefore, we develop the Hierarchical BERT Model (HBM) and combine it with certainty sampling. Experiments demonstrate that HBM outperforms state-of-the-art methods when labelled data is scarce, and it can work well with certainty sampling to reduce the workload associated with screening grey literature. For corpus comparison, we firstly compare the variants of Jensen-Shannon divergence (JSD) in the literature and identify JSD-pechenick as the appropriate variant to use in corpus comparison. Then we extend JSD-pechenick to enable a multi-corpus comparison. Lastly, we develop a Multi-corpus Topic-based Corpus Comparison (MTCC) approach by integrating topic modelling into corpus comparison. Based on the previous findings, we propose a pipeline that uses HBM+certainty and MTCC to support innovation scholars to explore and exploit agri-food innovation-related grey literature datasets.