篇名 | Identifying the Names of Complex Search Tasks with Task-Related Entities |
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卷期 | 21:1 |
作者 | Ting-Xuan Wang 、 Wen-Hsiang Lu |
頁次 | 069-089 |
關鍵字 | Complex Search Task 、 Task Name Identification 、 Task-related Entity 、 THCI Core |
出刊日期 | 201606 |
Conventional search engines usually consider a search query corresponding only to a simple task. Nevertheless, due to the explosive growth of web usage in recent years, more and more queries are driven by complex tasks. A complex task may consist of multiple sub-tasks. To accomplish a complex task, users may need to obtain information of various task-related entities corresponding to the sub-tasks. Users usually have to issue a series of queries for each entity during searching a complex search task. For example, the complex task “travel to Beijing” may involve several task-related entities, such as “hotel room,” “flight tickets,” and “maps”. Understanding complex tasks with task-related entities can allow a search engine to suggest integrated search results for each sub-task simultaneously. To understand and improve user behavior when searching a complex task, we propose an entity-driven complex task model (ECTM) based on exploiting microblogs and query logs. Experimental results show that our ECTM is effective in identifying the comprehensive task-related entities for a complex task and generates good quality complex task names based on the identified task-related entities.