Document Structure Driven Named Entity Recognition

该项目的长期目标是开发有效和高效的工具来检测和消除歧义 法律文件中的指定实体。命名实体是指符合以下条件的人员、组织、位置等 在文本中使用专有名词明确提到的。在文本中查找实体并为其分配 语义类型称为命名实体识别(NER)，通常与代词一起执行 决议。命名实体歧义消除(NED)是将文本中的命名实体链接到 在感兴趣的知识库中预先存在的对象，从而使提及的内容类似于 真实世界的实体。通常，这两个任务以流水线方式一起执行，也就是所谓的 实体解析和歧义消解(NERD)。 这个项目将实现两个相辅相成的目标。第一个是技术转让：我们将实施 申请人开发的命名实体识别和歧义消除(NERD)的现有算法以及 他的HQP作为加拿大计算的基础设施上的健壮和可伸缩的Web服务。第二个是开始一项 研究合作将导致一个更好的书呆子框架，能够处理法律术语。一把钥匙 改进将是使目前的Nerd工具使用现代图形存储和计算框架 例如阿帕奇的Gigraph，从而减少了它们的内存占用。此服务将由Compute托管 并将向加拿大所有感兴趣的研究人员提供。

The long-term goal of this project is developing effective and efficient tools for detecting and disambiguating named entities in legal documents. Named entities are the persons, organizations, locations, etc. that are explicitly mentioned in text using proper nouns. The task of finding entities in text and assigning them a semantic type is called Named Entity Recognition (NER), and is often performed in conjunction with pronoun resolution. Named Entity Disambiguation (NED) is the task of linking a named entity in the text to a pre-existing object in a knowledge base of interest, thus grounding the mention in something analogous to a real world entity. Often, both tasks are performed together in a pipelined fashion, in what is called Named Entity Resolution and Disambiguation (NERD). This project will accomplish two complimentary goals. The first is technology transfer: we will implement existing algorithms for Named Entity Recognition and Disambiguation (NERD) developed by the applicant and his HQP as robust and scalable Web services on Compute Canada's infrastructure. The second is to start a research collaboration that will lead to a better NERD framework, capable of handling legal jargon. A key improvement will be to make the current NERD tools use modern graph storage and computing frameworks such as Apache's Giraph, thus reducing their memory footprint. This service will be hosted by Compute Canada and will be made available to all interested researchers in Canada.