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The biolinguistics approach aims to construct a coherent and biologically plausible model/theory of human language as a computational system coded in the brain that for each individual recursively generates an infinite array of hierarchically structured expressions interpreted at the interfaces for thought and externalization. Language is a recent development in human evolution, is acquired reflexively from impoverished data, and shares common properties through the species in spite of individual diversity. Universal Grammar, a genuine explanation of language, must meet these apparently contradictory requirements. The Strong Minimalist Thesis (SMT) proposes that all phenomena of language have a principled account rooted in efficient computation, which makes language a perfect solution to interface conditions. LLMs, albeit their remarkable performance, cannot achieve the explanatory adequacy necessary for a language competence model. We implemented a computer model assuming these challenges, only using language-specific operations, relations, and procedures satisfying SMT. As a plausible model of human language, the implementation can put to test cutting-edge syntactic theory within the generative enterprise. Successful derivations obtained through the model signal the feasibility of the minimalist framework, shed light on specific proposals on the processing of structural ambiguity, and help to explore fundamental questions about the nature of the Workspace. 

To overcome the limitations of prevailing NLP methods, a Hybrid-Architecture Symbolic Parser and Neural Lexicon system is proposed to detect structural ambiguity by producing as many syntactic representations as there are interpretations for an utterance. HASPNeL comprises a symbolic AI, feature-unification parser, a lexicon generated using manual classification and machine learning, and a neural network encoder which tags each lexical item in a synthetic corpus and estimates likelihoods for each utterance’s interpretation with respect to the corpus. Language variation is accounted for by lexical adjustments in feature specifications and minimal parameter settings. Contrary to pure probabilistic system, HASPNeL’s neuro-symbolic architecture will perform grammaticality judgements of utterances that do not correspond to rankings of probabilistic systems; have a greater degree of system stability as it is not susceptible to perturbations in the training data; detect lexical and structural ambiguity by producing all possible grammatical representations regardless of their presence in the training data; eliminate the effects of diminishing returns, as it does not require massive amounts of annotated data, unavailable for underrepresented languages; avoid overparameterization and potential overfitting; test current syntactic theory by implementing a Minimalist grammar formalism; and model human language competence by satisfying conditions of learnability, evolvability, and universality.