End-to-end sequential metaphor identification inspired by linguistic theories

End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification

End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories

End-to-end training with Deep Neural Networks (DNN) is a currently popular methodfor metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theoriesof metaphor identification. We experimentwith two DNN models which are inspiredby two human metaphor identification procedures. By testing on three public datasets, wefind that our models achieve state-of-the-artperformance in end-to-end metaphor identification

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

2. Uluslararası Yapay Zeka ve Veri Bilimi Kongresi Bildiriler Kitabı

Çevrimiçi (127 sayfa : şekil, tablo ; 26 cm.)