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arXiv Computation and Language By Weiming Li, Catarina Barata, Miguel Constante, Joao Sanches

Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition

Read the original on arXiv Computation and Language →

The paper introduces a two‑stage framework for recognizing depression symptoms at the sentence level. First, a contrastively fine‑tuned sentence encoder generates a symptom candidate for each sentence. Then, a fine‑tuned language model verifies the candidate’s presence or absence by comparing the sentence, its context, and a diagnostic definition, ensuring the model’s judgment aligns with that definition before responding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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