Multitask Learning for Query Segmentation in Job Search

被引：4

作者：

Salehi, Bahar ^{[1
]}

Liu, Fei ^{[1
]}

Baldwin, Timothy ^{[1
]}

Wong, Wilson ^{[2
]}

机构：

[1] Univ Melbourne, Sch Comp & Informat Syst, Melbourne, Vic, Australia

[2] SEEK Ltd, Melbourne, Vic, Australia

来源：

PROCEEDINGS OF THE 2018 ACM SIGIR INTERNATIONAL CONFERENCE ON THEORY OF INFORMATION RETRIEVAL (ICTIR'18) | 2018年

关键词：

Query Segmentation; Word Embeddings; Neural Information Retrieval; Multitask Learning; Job Search;

D O I：

10.1145/3234944.3234965

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present the first attempt to use multitask learning for query segmentation. We use the semantic category of the words as an auxiliary task and show that segmentation improves when the model is also trained to predict the semantic category of the query terms, outperforming benchmark methods over a novel dataset from a popular job search engine. Our further experiments show that the task of modeling the query term semantics performs better as a standalone task, without adding segmentation as an auxiliary task.

引用

页码：179 / 182

页数：4

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