基于庞加莱嵌入的名词短语组合性预测
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
摘要：
多词短语的组合度是指一个短语的意义在多大程度上可以由其组成成分的意义及其语法关系推导出来。预测（非）组合性是一项经常使用分布式语义模型解决的任务。我们引入了一种新的技术，混合层次信息与分布信息预测组合。特别是，我们使用上位信息的多词和它的成分编码的形式，最近推出的庞加莱嵌入除了分布信息来检测组合名词短语。使用分布相似性的加权平均值和庞加莱相似性函数，我们在三个金标准数据集上获得了一致的、实质性的、统计学上显著的改进，这些改进超过了仅基于分布信息的最新模型。与传统方法仅使用无监督设置不同，我们还将问题框定为有监督的任务，获得了可比的改进。此外，我们公开发布了我们的庞加莱嵌入，这些嵌入是在大型语料库上手工制作的词汇-句法模式的输出上训练的。
英文摘要：
The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
