Identifying General Product and Brand Names in Online Forums

识别在线论坛中的通用产品和品牌名称

• 批准号: 521298-2017
• 负责人: Makrehchi, Masoud
• 金额: $ 1.82万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640129
• 关键词: Identifying; General; Product; Brand; Names

One key data component for engaging and acquiring customers is being able to identify names of products inonline forums. Therefore, the extraction of product/brand names will generate data to help vendors and thusincrease overall business revenue. The traditional approach to extract named entities requires time-consumingand expensive manual human-labelled training sets. In addition, we deal with many product or service sectorswhich come with different size, language, and content. Thus fitting a supervised model which is based onhuman-annotated data, for each product/brand to each sector (vertical) can be very expensive. Three majorchallenges are: 1) the high cost of generating training data for each type of product, 2) covering large divergenttypes of products from all verticals, and 3) disambiguating different types of entities based on context.Therefore, in the absence or lack of training data, alternative solution is semi-supervised learning algorithmssuch as bootstrapping and meta-learning methods such as self-training and co-training. In these methods, wecan train a model either with no or very few annotated data. In this research, a hybrid approach combiningtransfer learning and semi-supervised learning is investigated to identify and extract named entities in ourdomain of interest. In transfer learning, the solution for the lack of annotated data in the target domain, is toadapt annotated data from other domains.VerticalScope (the industry partner) is a Canadian company that is becoming a leading player in data scienceresearch and development for understanding user-generated content on the Internet in a variety of sectors. Theproposed project contributes to the growth of VerticalScope, and to the Canadian economy as a result, byallowing the company to apply cutting-edge research for improving user experience on their forums, and hencethe attractiveness of its service to the businesses.

吸引和获取客户的一个关键数据组件是能够在在线论坛中识别产品名称。因此，产品/品牌名称的提取将产生数据，以帮助供应商，从而增加整体业务收入。传统的命名实体提取方法需要耗时且昂贵的人工标注训练集。此外，我们处理许多产品或服务部门，这些部门具有不同的规模，语言和内容。因此，为每个产品/品牌的每个部门（垂直）拟合基于人工注释数据的监督模型可能非常昂贵。三大挑战是：1）为每种类型的产品生成训练数据的成本很高，2）覆盖来自所有垂直领域的大量不同类型的产品，以及3）基于上下文消除不同类型的实体的歧义。因此，在没有或缺乏训练数据的情况下，替代解决方案是半监督学习算法，如自举和元学习方法，如自训练和协同训练。在这些方法中，我们可以训练一个没有或很少注释数据的模型。在这项研究中，一个混合的方法相结合的迁移学习和半监督学习的研究，以识别和提取命名实体在我们感兴趣的领域。在迁移学习中，目标领域缺乏注释数据的解决方案是改编来自其他领域的注释数据。VerticalScope（行业合作伙伴）是一家加拿大公司，正在成为数据科学研究和开发的领先者，用于理解互联网上各个领域的用户生成内容。拟议的项目有助于VerticalScope的增长，并因此对加拿大经济做出贡献，允许该公司应用尖端研究来改善其论坛上的用户体验，从而提高其服务对企业的吸引力。