我应该参观这个地方吗？从评论中挖掘包含和排除短语
Should I visit this place? Inclusion and Exclusion Phrase Mining from Reviews
摘要：
虽然一些自动行程生成服务使旅行计划变得容易，但旅行者往往会发现自己处于无法充分利用旅行的独特情况。游客在许多因素方面有所不同，例如患有残疾，有特定的饮食偏好，与蹒跚学步的孩子一起旅行等。虽然大多数旅游景点都是普遍的，但其他景点可能不适合所有人。在本文中，我们专注于挖掘包含和排除短语与11个这样的因素，从评论相关的旅游景点的问题。虽然现有的工作主要集中在旅游数据采掘的结构化提取的行程相关信息，个性化的情感分析，和自动行程生成，据我们所知，这是第一个工作的纳入/排除短语挖掘旅游评论。使用与1000个旅游景点相关的2000条评论的数据集，我们的广义水平分类器提供了$\sim$ 80和$\sim$ 82的二元重叠F1，以分别将短语分类为包含或排除。此外，我们的包含/排除分类器分别为11类包含和排除分类提供了$\sim$ 98和$\sim$ 97的F1。我们相信，我们的工作可以显着提高自动行程生成服务的质量。
英文摘要：
Although several automatic itinerary generation services have made travel planning easy, often times travellers find themselves in unique situations where they cannot make the best out of their trip. Visitors differ in terms of many factors such as suffering from a disability, being of a particular dietary preference, travelling with a toddler, etc. While most tourist spots are universal, others may not be inclusive for all. In this paper, we focus on the problem of mining inclusion and exclusion phrases associated with 11 such factors, from reviews related to a tourist spot. While existing work on tourism data mining mainly focuses on structured extraction of trip related information, personalized sentiment analysis, and automatic itinerary generation, to the best of our knowledge this is the first work on inclusion/exclusion phrase mining from tourism reviews. Using a dataset of 2000 reviews related to 1000 tourist spots, our broad level classifier provides a binary overlap F1 of $\sim$80 and $\sim$82 to classify a phrase as inclusion or exclusion respectively. Further, our inclusion/exclusion classifier provides an F1 of $\sim$98 and $\sim$97 for 11-class inclusion and exclusion classification respectively. We believe that our work can significantly improve the quality of an automatic itinerary generation service.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
