반응형 자연어처리572 [2022-12-01] 오늘의 자연어처리 Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding mod.. 2022. 12. 1. [2022-11-30] 오늘의 자연어처리 Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers Recent work has demonstrated that natural language processing techniques can support consumer protection by automatically detecting unfair clauses in the Terms of Service (ToS) Agreement. This work demonstrates that transformer-based ToS analysis systems are vulnerable to adversarial attacks. We conduct expe.. 2022. 11. 30. [2022-11-29] 오늘의 자연어처리 Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequent.. 2022. 11. 29. [2022-11-28] 오늘의 자연어처리 Embedding Compression for Text Classification Using Dictionary Screening In this paper, we propose a dictionary screening method for embedding compression in text classification tasks. The key purpose of this method is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads .. 2022. 11. 28. 이전 1 ··· 104 105 106 107 108 109 110 ··· 143 다음 반응형