A systemic functional linguistics approach to implicit entity recognition in tweets. Issue 4 (July 2022)

• Record Type: Journal Article
• Authors: Hosseini, Hawre; Mansouri, Mehran; Bagheri, Ebrahim
• Is Part Of: Information processing & management. Volume 59:Issue 4(2022)
• Publication Date: 2022-07
• Subjects: Named entity recognition -- Tweet analytics -- Knowledge graphs -- Computational linguistics; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102957
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique. Highlights: Formulate implicit entity recognition exploiting systemic functional linguistics. Provide a systematic categorization of syntagmatic and paradigmatic classes of features. Curate labeled resources, e.g. process types, and publicly share such resources. Provide detailed feature performance and error analysis of our work for better explainability.