文献类型：期刊文献

英文题名：Noun Compound Interpretation With Relation Classification and Paraphrasing

作者：Liu, Jingping[1];Liu, Juntao[2];Chen, Lihan[2];Liang, Jiaqing[2];Xiao, Yanghua[2];Xu, Huimin[3];Zhang, Fubao[3];Wang, Zongyu[3];Xie, Rui[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200231, Peoples R China;[2]Fudan Univ, Sch Comp Sci, Shanghai 200437, Peoples R China;[3]Meituan, Shanghai 20035, Peoples R China

年份：2023

卷号：35

期号：9

起止页码：8757

外文期刊名：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

收录：;EI(收录号：20224112870194);WOS:【SCI-EXPANDED(收录号:WOS:001045704800005)】；

基金：This work was supported in part by the National Key Research and Development Project under Grant 2020AAA0109302, in part by Shanghai Science and Technology Innovation Action Plan under Grant 19511120400, and in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0103.

语种：英文

外文关键词：Noun compound interpretation; relation classification; paraphrasing

摘要：Noun compounds are abundant in various languages and their interpretations have been applied in a wide range of NLP tasks. However, most existing work only uses relation classification- or paraphrasing-based methods to model this problem, failing in coverage or accuracy. We argue that the above two approaches are complementary to each other for the noun compound interpretation. In this paper, we propose a two-phase strategy to solve this task. The first phase is to perform the relation classification sub-task with a novel multi-view representation learning model. When noun compounds are predicted as the non-semantic relation, i.e., NA, or the confidence scores are below the threshold, the second phase, namely paraphrasing, will be triggered to interpret noun compounds with a contrastive slot filling method. To evaluate the effectiveness of our methods, we construct the largest Chinese dataset for noun compound interpretation in the life service domain. The experimental results on our constructed and public datasets prove the effectiveness of our solution. Furthermore, the online A/B testing on Meituan APP suggests that the Query View Click-Through Rate increases by 0.91% when noun compounds are used to enrich semantic information of items with the help of their interpretations on the platform.