CrowdGather：结构化域上的实体抽取
CrowdGather: Entity Extraction over Structured Domains
摘要：
众包实体提取通常用于为许多应用获取数据，包括推荐系统、聚合列表和目录的构建以及知识库构建。当前的解决方案集中于使用单个查询的实体提取，例如，在收集所有餐厅的列表时，仅使用“give me another restaurant”。由于人力成本，专注于单个查询的解决方案可能非常不切实际。在本文中，我们利用实体提取通常集中在{ \em结构域}的事实，即，由属性集合描述的域，每个域都可能表现出层次结构。给定这样的域，我们可以实现更丰富的查询空间，例如，“再给我一家曼哈顿的摩洛哥餐厅，叫外卖”。当然，启用更丰富的查询空间会带来许多问题，特别是因为许多查询返回空答案。我们开发了新的统计工具，使我们能够推理的收益发出{ \em额外的查询}几乎没有信息，并显示我们如何可以利用重叠的查询结果的数据域的不同点，以获得准确的估计增益。我们把{ \em预算实体提取}的问题作为一个自适应优化问题来处理，该问题寻求最大化提取实体的数量，同时最小化整体提取成本。我们通过在合成和真实世界数据集上的实验来评估我们的技术，证明在相同预算下，产量高达竞争方法的4倍。
英文摘要：
Crowdsourced entity extraction is often used to acquire data for many applications, including recommendation systems, construction of aggregated listings and directories, and knowledge base construction. Current solutions focus on entity extraction using a single query, e.g., only using "give me another restaurant", when assembling a list of all restaurants. Due to the cost of human labor, solutions that focus on a single query can be highly impractical. In this paper, we leverage the fact that entity extraction often focuses on {\em structured domains}, i.e., domains that are described by a collection of attributes, each potentially exhibiting hierarchical structure. Given such a domain, we enable a richer space of queries, e.g., "give me another Moroccan restaurant in Manhattan that does takeout". Naturally, enabling a richer space of queries comes with a host of issues, especially since many queries return empty answers. We develop new statistical tools that enable us to reason about the gain of issuing {\em additional queries} given little to no information, and show how we can exploit the overlaps across the results of queries for different points of the data domain to obtain accurate estimates of the gain. We cast the problem of {\em budgeted entity extraction} over large domains as an adaptive optimization problem that seeks to maximize the number of extracted entities, while minimizing the overall extraction costs. We evaluate our techniques with experiments on both synthetic and real-world datasets, demonstrating a yield of up to 4X over competing approaches for the same budget.
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。
