Detecting ongoing events using contextual word and sentence embeddings. (15th December 2022)

• Record Type: Journal Article
• Authors: Maisonnave, Mariano; Delbianco, Fernando; Tohmé, Fernando; Maguitman, Ana; Milios, Evangelos
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Ongoing Event Detection -- Information Extraction -- Contextual embeddings -- BERT -- RNN -- CNN; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118257
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This paper introduces the Ongoing Event Detection (OED) task, which is a specific Event Detection task where the goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current. Any application that needs to extract structured information about ongoing events from unstructured texts can take advantage of an OED system. The main contribution of this paper are the following: (1) it introduces the OED task along with a dataset manually labeled for the task; (2) it presents the design and implementation of an RNN model for the task that uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for detecting ongoing events in news; (3) it presents an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model. The results offer several insights into the importance of contextual embeddings and indicate that the proposed approach is effective in the OED task, outperforming the baseline models. Highlights: The Ongoing Event Detection (OED) task is defined. A dataset for the task is made available, along with extensive documentation. Baselines for the task using classical and state-of-the-art techniques are reported. A proposed model that uses contextual word and sentence embeddings is presented. An extensive empirical evaluation is reported with several variations of the models.