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This paper presents a benchmark for SNOMED CT concept recommendation from masked clinical contexts, comparing sparse, dense, and retrieval-augmented approaches, and shows that sparse TF-IDF performs best in low-resource settings.
This paper proposes an error-aware TF-IDF retrieval-augmented generation framework for correcting automatic speech recognition errors, achieving significant accuracy gains on Persian FLEURS with near-zero inference latency.
This paper uses TF-IDF and neural models on the Complete Tang Poems to predict poets' regional origins from linguistic features, finding detectable regional fingerprints, distance-decay effects, and temporal modulation of the signal.