Entity Mention
Entity mention, the identification and classification of named entities within text, is a core task in natural language processing aiming to extract structured information from unstructured data. Current research focuses on improving accuracy and robustness across diverse domains and languages, employing techniques like transformer-based models, graph neural networks, and knowledge base integration to enhance entity recognition and relation extraction. This work is crucial for applications ranging from knowledge graph construction and question answering to improved search and information retrieval, impacting various fields including legal tech, biomedical research, and financial analysis. Furthermore, ongoing efforts address challenges like handling ambiguous entity mentions, hallucinations in large language models, and the efficient processing of low-resource languages.
Papers
FlairNLP at SemEval-2023 Task 6b: Extraction of Legal Named Entities from Legal Texts using Contextual String Embeddings
Vinay N Ramesh, Rohan Eswara
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models
Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty
Causal Reasoning of Entities and Events in Procedural Texts
Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, Chris Callison-Burch