We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing semantic relatedness. Combined with a common similarity measure, LexNET performs fairly good on the word relatedness task (subtask 1). The relatively lower performance of LexNET and the various other systems on subtask 2, however, confirms the difficulty of the semantic relation classification task, and stresses the need to develop additional methods for this task.

本文提出了一个基于网络算法LexNET的语义关系识别方法，其中结合了路径和分布式信息，在CogALex 2016语义关系公共任务中获得了良好的成绩。虽然在一些子任务中表现相对较差，但仍然显示出路径和分布式信息相结合对于语义关系的识别意义重大。

CogALex-V 共享任务: LexNET - 用于语义关系识别的综合路径和分布式方法