论文标题
暗示语义解析使用统计单词感官歧义
Hinting Semantic Parsing with Statistical Word Sense Disambiguation
论文作者
论文摘要
语义解析的任务可以近似为将话语转换为逻辑形式图的转换，其中边缘代表语义角色，节点代表单词感官。由此产生的表示应该是捕获话语的含义并适合推理。单词感官和语义角色是相互依存的，这意味着分配单词感官时的错误可能会导致分配语义角色的错误，反之亦然。尽管统计方法的统计方法歧义差异优于逻辑，基于规则的语义解析器用于原始单词sission分配，但这些统计单词sense sissens disampaulion歧义系统并未产生丰富的作用结构或输入的详细语义表示。在这项工作中，我们提供来自统计WSD系统的提示，以指导逻辑语义解析器，以产生更好的语义类型作业，同时保持所得逻辑形式的合理性。我们观察到F-Score的提高高达10.5％，但是我们发现这种改进是为了支付解析的结构完整性的代价
The task of Semantic Parsing can be approximated as a transformation of an utterance into a logical form graph where edges represent semantic roles and nodes represent word senses. The resulting representation should be capture the meaning of the utterance and be suitable for reasoning. Word senses and semantic roles are interdependent, meaning errors in assigning word senses can cause errors in assigning semantic roles and vice versa. While statistical approaches to word sense disambiguation outperform logical, rule-based semantic parsers for raw word sense assignment, these statistical word sense disambiguation systems do not produce the rich role structure or detailed semantic representation of the input. In this work, we provide hints from a statistical WSD system to guide a logical semantic parser to produce better semantic type assignments while maintaining the soundness of the resulting logical forms. We observe an improvement of up to 10.5% in F-score, however we find that this improvement comes at a cost to the structural integrity of the parse
