RI: Small: Learning Open Domain Semantic Parsers

RI：小型：学习开放域语义解析器

• 批准号: 1218692
• 负责人: Alexander Yates
• 金额: $ 42.58万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218692&HistoricalAwards=false
• 关键词: RI; Small; Learning; Open; Domain

Supervised semantic parsers, which learn to map language to relational data, perform poorly on texts that differ in vocabulary or style from the training text, and on databases that differ from the database used in training. Today's semantic parsers have only been tested on narrowly-circumscribed domains like geography, but the ideal semantic parser would generalize to the many and incredibly diverse relational databases available on the Web. This project develops semantic parsers that approach this ideal system. The project divides the overall task into two parts: mapping named-entities in text to database constants in any domain, and mapping full sentences and questions to logical forms written in a variant of the lambda calculus. Techniques for resolving named-entities make use of domain-independent contextual information around the named-entity for disambiguation. To connect words like "directed" with a database relation listing directors of movies, the project relies on schema-matching techniques from database integration. The system extracts a relational view of a corpus, and then generates alignments between these extracted alignments and the fixed relational structure of existing databases. The project uses transfer-learning and co-training approaches to estimate parameters for statistical models for named-entity disambiguation and schema matching across domains and databases.The project is expected to produce new methods and software systems for connecting human language with relational data. It enables language-based queries to the broad array of structured data available on the Web, making it easier to find information than ever before.

监督语义解析器学习将语言映射到关系数据，在词汇或风格与训练文本不同的文本上以及在与训练中使用的数据库不同的数据库上表现不佳。 今天的语义解析器只在地理等狭窄的领域进行了测试，但理想的语义解析器将推广到Web上许多令人难以置信的各种关系数据库。 这个项目开发了接近这个理想系统的语义解析器。 该项目将整个任务分为两部分：将文本中的命名实体映射到任何域中的数据库常量，并将完整的句子和问题映射到用lambda演算的变体编写的逻辑形式。 用于解析命名实体的技术利用围绕命名实体的与域无关的上下文信息来消除歧义。 为了将像“directed”这样的词与列出电影导演的数据库关系连接起来，该项目依赖于数据库集成中的模式匹配技术。 该系统提取语料库的关系视图，然后在这些提取的对齐与现有数据库的固定关系结构之间生成对齐。 该项目使用迁移学习和协同训练方法来估计用于跨域和数据库的命名实体消歧和模式匹配的统计模型的参数。该项目预计将产生连接人类语言与关系数据的新方法和软件系统。 它支持对Web上可用的大量结构化数据进行基于语言的查询，使查找信息比以往任何时候都更容易。