This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.

本文介绍了一个为参与SemEval-2018任务9的超超级词发现系统，通过预设的词汇表的搜索空间给出最佳的候选超系词。我们引入了神经网络架构来完成这个任务，并且实证研究了各种神经网络模型以建立单词和短语的潜在空间表示。评估模型包括卷积神经网络、长短期记忆网络、门控循环单元和循环卷积神经网络。我们还探讨了不同的嵌入方法，包括单词嵌入和感性嵌入，以取得更好的性能。

上海交通大学自然语言处理实验室参加SemEval-2018任务9: 利用术语嵌入进行神经上下位关系发现