• Natural Language Processing (NLP) early work centers on formal grammar-driven parsing to produce deep structural representations from sentences, using augmented transition networks, CFG formalism, and time-bounded parsing to couple syntax with semantic interpretation [5, 7, 13, 16].

• Natural Language Processing (NLP) early perception and lexical processing treats word recognition as rapid perceptual decisions, leveraging dynamic programming, time normalization, phonemic recoding, and contextual cues such as binary digrams to improve recognition and disambiguation in both speech and text, i.e., NLP approaches [4, 10, 17, 19, 20].

• Natural Language Processing (NLP) semantic representations and knowledge-based understanding emphasize mapping from linguistic structure to meaning through conceptual dependency, semantics, and heuristic understanders, tying syntax to cognitive meaning in NLP [1, 9, 14, 15, 18].

• Natural Language Processing (NLP) dialogue and human-computer interaction explore rule-based conversational systems and keyword-triggered responses that enable man-machine communication, as seen in ELIZA and related work in NLP interfaces [11, 12].

• Natural Language Processing (NLP) lexical semantics and cognitive processing connect concreteness, imagery, and meaningfulness with lexical representations and semantic evaluation, bridging distributional semantics and morphology in NLP systems [1, 3, 15, 17].