| """Minimal answer cleanup for Medico 2026 Task 1 submissions. |
| |
| Full-test rescoring showed that semantic or qtype-specific normalization can |
| reduce the official text metrics. Keep this module intentionally conservative: |
| only remove obvious formatting noise and never rewrite the medical meaning. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import re |
|
|
| _SPACE_RE = re.compile(r"\s+") |
| _LEADING_ANSWER_RE = re.compile(r"^\s*(?:answer\s*[:\-]\s*)+", re.IGNORECASE) |
|
|
|
|
| def compact_spaces(text: str) -> str: |
| return _SPACE_RE.sub(" ", str(text or "")).strip() |
|
|
|
|
| def normalize_prediction(prediction: str, question: str = "") -> str: |
| """Return a minimally cleaned prediction without semantic rewrites.""" |
|
|
| del question |
| text = compact_spaces(prediction) |
| text = _LEADING_ANSWER_RE.sub("", text) |
| text = re.sub(r"\s+([,.;:!?])", r"\1", text) |
| return compact_spaces(text).strip() |
|
|
|
|
| def infer_question_type_for_normalization(question: str) -> str: |
| """Compatibility shim for older imports; not used for rewriting.""" |
|
|
| q = str(question or "").lower() |
| if any(marker in q for marker in ["how many", "number of", "count"]): |
| return "numerical_count" |
| if q.startswith(("is ", "are ", "was ", "were ", "do ", "does ", "did ", "can ", "has ", "have ")): |
| return "yes_no" |
| return "other" |
|
|
|
|
| __all__ = ["normalize_prediction", "infer_question_type_for_normalization"] |
|
|
