This paper presents a joint syntactic-semantic embedding model which not only uses syntactic information to enrich the word embeddings but also generates distributed representations for the syntactic structures themselves. The syntactic input to our model comes from a Lexicalized Tree-Adjoining Grammar parser. The word embeddings from our model outperform the Skip-gram embeddings in several word similarity and sentiment classification experiments. The syntactic structure embeddings help improve a transition-based dependency parser by a clear margin.

中文翻译：

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基于词法化的树邻接语法的组合句法语义嵌入模型

本文提出了一种联合的句法-语义嵌入模型，该模型不仅利用句法信息丰富词的嵌入，而且还为句法结构本身生成分布式表示。该模型的语法输入来自词法化的树状语法分析器。在几个单词相似度和情感分类实验中，我们模型中的单词嵌入优于Skip-gram嵌入。语法结构嵌入有助于明显改善基于过渡的依存解析器。