Textual entailment is a semantic inference task that determines whether a hypothesis can be logically inferred from a premise. Although widely studied in general-domain natural language inference, its application to software requirement specifications remains challenging due to limited annotated data, domain-specific language, and class imbalance.This paper proposes a structured and reproducible methodological framework for identifying textual entailment in software requirement documents by leveraging structured software artifacts and deep learning techniques. Premise-hypothesis pairs are derived from Use Case Diagrams and Use Case Specifications using logical entailment rules, reducing dependence on large-scale manual annotation. Deep learning models are trained with word embeddings and ensemble learning strategies to improve robustness under limited and imbalanced data conditions. The methodology is validated across multiple stages, including rule-based labeling verification, dataset distribution analysis, model configuration assessment, and performance evaluation using confusion-matrix. This study provides a practical guide for applying textual entailment in specialized domains with constrained data availability.