Neural networks for NLP are becoming increasingly complex and widespread, there is a growing concern if these models responsible to use. Explaining helps address the safety ethical concerns essential accountability. Interpretability serves provide explanations in terms that understandable humans. Additionally, post-hoc methods after model learned generally model-agnostic. This survey provides c...

Advanced NLP Procedures as Premises for the Reconstruction of Idea Knowledge The article presents current state development Natural Language Processing (NLP) technology, in particular GPT-3 language model, and its consequences understanding phenomenon knowledge. technology has been experiencing remarkable recently. model a level advancement that allows it to generate texts answers general quest...

We present a framework for building a medical natural language processing (NLP) system capable of deep understanding of clinical text reports. The framework helps developers understand how various NLP-related efforts and knowledge sources can be integrated. The aspects considered include: 1) computational issues dealing with defining layers of intermediate semantic structures to reduce the dime...

Many existing integrations of natural language processing (NLP) and knowledgebased systems build on "shallow" NLP. Prominent examples are term-frequency based document retrieval or document topic extraction systems. Recent progress in NLP, however, has brought "deep" processing features like sentential parsing and semantic dependency analysis to a highly mature level for a substantial set of co...