feat(data): 漏斗第一层词表 funnel_lexicon——八类事件+否定前缀守卫, sentiment_lexicon 范式 [nas] [no-doc]

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+# -*- coding: utf-8 -*-
+"""funnel_lexicon.py — 漏斗第一层确定性事件词表(spec §4.8 F1, P4-3).
+
+范式照 sentiment_lexicon(子串零分词+可解释可审计);升级=词表加行非代码改.
+否定前缀守卫拦「不减持/终止回购/解除质押误入 pledge_new」类误命中;v1 接受
+残余噪声换确定性,方向=分类学固有先验不做上下文翻转(ambiguous 边界判给
+第二层 LLM, 见 corpus_funnel.is_llm_candidate).
+"""
+from __future__ import annotations
+
+# 事件分类学 pilot 八类(code 稳定供下游枚举校验;扩充=加行)
+EVENT_TYPES: dict[str, str] = {
+    "holder_increase": "股东增持",
+    "holder_decrease": "股东减持",
+    "buyback": "回购",
+    "pledge_new": "新增质押",
+    "pledge_release": "解除质押",
+    "earnings_pre_up": "业绩预增",
+    "earnings_pre_down": "业绩预减或预亏",
+    "investigation": "立案调查或处罚",
+}
+
+# 方向先验(pos/neg), 分类学固有
+EVENT_DIRECTION: dict[str, str] = {
+    "holder_increase": "pos", "holder_decrease": "neg", "buyback": "pos",
+    "pledge_new": "neg", "pledge_release": "pos", "earnings_pre_up": "pos",
+    "earnings_pre_down": "neg", "investigation": "neg",
+}
+
+# 词表(命中即该类型;否定前缀守卫在 _hit)
+LEXICON: dict[str, tuple[str, ...]] = {
+    "pledge_release": ("解除质押", "解押"),
+    "holder_increase": ("拟增持", "增持计划", "股东增持", "继续增持",
+                        "增持公司股份", "累计增持"),
+    "holder_decrease": ("拟减持", "减持计划", "股东减持", "继续减持",
+                        "减持公司股份", "累计减持"),
+    "buyback": ("回购股份", "回购公司股份", "回购报告书", "回购实施"),
+    "pledge_new": ("质押",),
+    "earnings_pre_up": ("业绩预增", "预增", "扭亏"),
+    "earnings_pre_down": ("业绩预减", "预亏", "预减", "首亏", "续亏"),
+    "investigation": ("立案", "行政处罚", "警示函", "监管函"),
+}
+
+# 否定/动作撤销前缀(命中词前 2 字窗口含此列 → 该命中作废)
+NEGATION_PREFIXES: tuple[str, ...] = ("不", "未", "终止", "取消", "解除")
+
+# LLM 候选信号词(词表零命中但含任一 → 值得第二层看一眼)
+SIGNAL_WORDS: tuple[str, ...] = (
+    "增持", "减持", "回购", "质押", "业绩", "预增", "预亏", "立案",
+    "处罚", "中标", "合同", "重组", "收购", "分红", "退市", "违规",
+    "担保", "诉讼", "仲裁", "冻结",
+)
+
+
+def _hit(text: str, kw: str) -> bool:
+    """kw 命中且前 2 字窗口不含否定前缀(「不减持」「解除质押」里的裸命中作废)."""
+    i = text.find(kw)
+    while i != -1:
+        prefix = text[max(0, i - 2):i]
+        if not any(prefix.endswith(n) for n in NEGATION_PREFIXES):
+            return True
+        i = text.find(kw, i + 1)
+    return False
+
+
+def match_events(title: str, text: str | None = None) -> list[dict]:
+    """词表命中(类型去重) → [{event_type, direction, confidence: 1.0}]."""
+    blob = f"{title or ''}\n{text or ''}"
+    out = []
+    for etype, kws in LEXICON.items():
+        if any(_hit(blob, kw) for kw in kws):
+            out.append({"event_type": etype,
+                        "direction": EVENT_DIRECTION[etype],
+                        "confidence": 1.0})
+    return out
+
+
+def is_llm_candidate(title: str, text: str | None = None) -> bool:
+    """词表零命中但含信号词 → 第二层候选(简单子串无守卫, 边界判给 LLM)."""
+    if match_events(title, text):
+        return False
+    blob = f"{title or ''}\n{text or ''}"
+    return any(kw in blob for kw in SIGNAL_WORDS)