用语义信息优化无监督句法分析，提升解析准确率。
Improving Unsupervised Constituency Parsing via Maximizing Semantic Information
- 以语义信息（SemInfo）替代传统似然目标，增强结构与语义关联
- 在五种PCFG变体、四种语言上平均提升7.85分句F1值
- 适合关注无监督句法分析与语义建模的研究者
无监督句法分析器将句子中的短语组织成树状句法结构，反映句子的语义组织。然而，传统最大化句子对数似然（LL）的目标未显式考虑句法结构与语义之间的紧密关系，导致LL值与解析准确率相关性较弱。本文提出一种新目标——最大化构成结构中编码的语义信息（SemInfo），引入词袋子串模型表示语义，并通过概率加权信息度量估计SemInfo值。将SemInfo最大化目标应用于概率上下文无关文法（PCFG）解析器训练，并设计基于树条件随机场（TreeCRF）的模型以支持训练。实验表明，SemInfo与解析准确率的相关性高于LL，可作为更优的无监督解析目标。所提算法在五种PCFG变体和四种语言上平均提升7.85句F1得分，其中三种语言达到当前最优水平。
原文摘要 · Abstract (English)
Unsupervised constituency parsers organize phrases within a sentence into a tree-shaped syntactic constituent structure that reflects the organization of sentence semantics. However, the traditional objective of maximizing sentence log-likelihood (LL) does not explicitly account for the close relationship between the constituent structure and the semantics, resulting in a weak correlation between LL values and parsing accuracy. In this paper, we introduce a novel objective that trains parsers by maximizing SemInfo, the semantic information encoded in constituent structures. We introduce a bag-of-substrings model to represent the semantics and estimate the SemInfo value using the probability-weighted information metric. We apply the SemInfo maximization objective to training Probabilistic Context-Free Grammar (PCFG) parsers and develop a Tree Conditional Random Field (TreeCRF)-based model to facilitate the training. Experiments show that SemInfo correlates more strongly with parsing accuracy than LL, establishing SemInfo as a better unsupervised parsing objective. As a result, our algorithm significantly improves parsing accuracy by an average of 7.85 sentence-F1 scores across five PCFG variants and in four languages, achieving state-of-the-art level results in three of the four languages.
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