AI 中文总结
研究非二元自底向上成分句法分析，提出新方法，通过分别引入成分标签并从定界符界定的栈配置恢复规约跨度，消除特定于标签-元数的规约动作，实验表明该方法在相同框架下有竞争力且高元数成分不受影响。
AI 中文摘要
非二元自底向上成分句法分析通常被认为需要元数操作：如 \(\textsc{Reduce-}X\#k\) 这样的规约操作既指定了母节点标签，又指定了要组合的子节点数量。我们表明这个元数参数并非必要的转换原语。我们的句法分析器分别引入成分标签，并从由定界符界定的栈配置中恢复规约跨度。在一个格式良好的规约配置中，元数由活动定界符和标签标记唯一确定，使其成为句法分析器状态的派生属性而非动作标签。这种分解消除了特定于标签-元数的规约动作，同时保留了原始非二元树的直接构建。在宾州树库（PTB）和汉语树库（CTB）上的实验表明，在相同实现框架下，定界符引导的句法分析器与特定元数的自底向上基线相比仍具有竞争力，且动作集要小得多。分析进一步表明，其预测的元数分布与黄金树库接近，并且去除元数操作后高元数成分不会崩溃。
英文摘要
Non-binary bottom-up constituency parsing commonly uses arity-specific reductions such as \(\textsc{Reduce-}X\#k\), which jointly specify the mother label and number of children. We show that arity need not be a primitive transition parameter. The proposed parser predicts the mother label separately and identifies the ordered child sequence from a delimiter-bounded stack configuration. In every well-formed reducible configuration, the active delimiter, label marker, and intervening completed items uniquely determine the reduction domain and hence its arity. This factorization removes label--arity-specific reduction actions while preserving direct construction of the original non-binary trees. Controlled experiments on PTB and CTB show that the delimiter-guided parser closely tracks an arity-specific baseline while using a substantially smaller action inventory. Its predicted trees retain the gold branching profile without systematic collapse at higher arities. The smaller inventory is obtained at the cost of longer derivations and boundary-sensitive errors involving delimiter insertion, preservation, and consumption.
