Beyond the Benchmarks: Toward Human-Like Lexical Representations.

Area of Science:

• Computational linguistics
• Cognitive science
• Psycholinguistics

Background:

• Effective human-computer interaction necessitates computational models of language that align with human cognitive expectations.
• The human lexicon is characterized by richly structured semantic representations, continuous adaptability, and grounding in universal conceptualization.
• Current natural language processing (NLP) models often struggle to fully capture these nuanced properties of word-level semantics.

Purpose of the Study:

• To analyze the core properties of the human lexicon relevant to computational modeling.
• To evaluate the extent to which current NLP techniques instantiate these properties.
• To propose new directions for NLP inspired by linguistic and cognitive science.

Main Methods:

• Literature review and conceptual analysis of lexical semantics.
• Assessment of state-of-the-art NLP models against key desiderata of semantic representation.
• Synthesis of insights from language sciences to inform computational approaches.

Main Results:

• The human lexicon exhibits complex semantic structures, dynamic adaptability, and cross-linguistic conceptual grounding.
• Existing NLP methods show limitations in fully replicating these human-like semantic capabilities.
• There is a significant gap between current NLP and the requirements for human-compatible language processing.

Conclusions:

• Advancing NLP requires deeper integration of principles from psycholinguistics and cognitive science.
• Future computational models should prioritize dynamic, conceptually grounded semantic representations.
• Inspiration from the language sciences is crucial for developing more sophisticated and human-like NLP systems.