DeFacto: Acquiring, Curating, and Using a Bilingual Domain Aware Commonsense Knowledge Base

DeFacto：获取、整理和使用双语领域感知常识知识库

• 批准号: RGPIN-2017-05068
• 负责人: Langlais, Philippe
• 金额: $ 1.68万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679208
• 关键词: DeFacto; Acquiring; Curating; Using; Bilingual

Automatically extracting knowledge from a large set of mostly unstructured documents (such as the Web) and organizing it into a knowledge base (KB) is a key challenge in artificial intelligence. Intuitively, such KBs should directly impact the quality of many NLP applications such as question answering, information retrieval or Text Analytics. Open information extraction, the task of extracting knowledge from texts without much supervision (especially not a prescription of the kind of information to mine), has brought new hope for such an endeavour. ******Despite a number of well-designed components are nowadays widespread and readily available for extracting facts and relations (so-called tuples) from texts, tapping information in large collections of texts still raises a number of issues. The technology embedded in a typical knowledge extraction pipeline is fraught with shortcomings: coreference resolution, named-entity resolution and parsing errors are collapsing so that many tuples (if not the vast majority) are simply useless. Also, most works are targeting very frequent entities and relations, which exclude a large quantity of information on domain specific texts that are pervasive over the Web. ******Our long term objective consists in developing the necessary expertise in populating, curating, maintaining and using a KB. Our proposal departs from several existing initiatives by a number of key factors. First, since specific domains are prevalent over the Web, we want our technology to be domain aware. Second, since today's world is multi-lingual and because not everything is written in English, we further want our technology to be multi-lingual in nature. Last, most works are devoted to develop fully automatic technology for assisting humans. In our proposal, we are interested in measuring how much gaming with a purpose can make humans assist the computer. ******In order to succeed, we target in this proposal the development of deFacto, a multi-domain, bilingual KB (French -- English) acquired iteratively from texts mined over the web, with the help of feedback collected from users via serious gaming.

从大量非结构化文档（如Web）中自动提取知识并将其组织成知识库（KB）是人工智能中的一个关键挑战。直观地说，这样的KBs应该直接影响许多NLP应用程序的质量，如问答、信息检索或文本分析。开放信息提取，即在没有太多监督的情况下从文本中提取知识的任务（特别是没有对信息进行挖掘的规定），为这种努力带来了新的希望。******尽管现在有许多设计良好的组件广泛使用，并且可以很容易地从文本中提取事实和关系（所谓的元组），但是在大量文本集合中挖掘信息仍然会引起许多问题。嵌入在典型知识提取管道中的技术充满了缺点：共同引用解析、命名实体解析和解析错误正在崩溃，因此许多元组（如果不是绝大多数的话）根本没用。此外，大多数作品针对的是非常频繁的实体和关系，这就排除了大量在网络上普遍存在的特定领域文本的信息。******我们的长期目标包括发展必要的专业知识，在人口，策划，维护和使用知识库。我们的建议在若干关键因素上有别于若干现有倡议。首先，由于特定的域在Web上普遍存在，我们希望我们的技术具有域感知能力。第二，由于今天的世界是多语言的，而且不是所有的东西都是用英语写的，我们进一步希望我们的技术在本质上是多语言的。最后，大部分工作致力于开发全自动技术来帮助人类。在我们的提议中，我们感兴趣的是衡量有目的的游戏能在多大程度上使人类帮助计算机。******为了取得成功，我们的目标是开发deFacto，这是一种多领域的双语知识库（法语-英语），通过从用户通过严肃游戏收集的反馈，从网络上挖掘的文本中迭代获取。