Semantic Analysis of Technical Documentation: Systematic Review, Formal Task Definition, and Transformer-Based NER Implementation

The increasing complexity and volume of technical documentation, includ-ing requirements specifications, patents, and engineering reports, creates significant challenges for manual analysis and knowledge extraction. This paper includes a systematic review of methods for semantic content analy-sis of technical documents, with a particular focus on Natural Language Processing (NLP) techniques and Transformer-based models. The study formalizes the task of structured information extraction and provides a mathematical description of Named Entity Recognition (NER) as a core subtask. A practical case study demonstrates an end-to-end NER pipeline for Russian-language technical requirements, leveraging ruRoberta-large via spaCy-transformers. The results highlight both the potential and limitations of current approaches, emphasizing the critical role of annotation con-sistency and document format normalization. This work contributes to the development of intelligent systems for engineering documentation analysis and outlines key directions for future research.