• Natural Language Processing (NLP) rests on cognitive-semantic models that explain how semantic representations are activated and integrated during reading and text comprehension, spanning word-level to discourse-level processing [1], [3], [11], [9], [5].

• In NLP and psycholinguistics, word recognition is strongly modulated by context, with priming, retrieval dependencies, and ambiguity resolution guiding recognition across sentences and words [2], [13], [17], [9].

• Natural Language Processing (NLP) parsing advances via transition-network grammars, statistical decoders, and procedural semantics, combining deep representations with constraint-based processing [4], [15], [6], [11], [16].

• Information Retrieval and text processing foundations arise from statistical term specificity, indexing evaluation, and contextual error-correction, shaping retrieval effectiveness and domain adaptation [7], [10], [12], [19].

• Question answering and natural language generation bridge language models and databases, emphasizing procedural semantics and generation strategies for data-driven QA and explanation [6], [18], [14], [15].