论文笔记5：Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns
使用条件随机场抽取信息，抽取实体间的二元关系 一、论文要解决的问题 在信息抽取系统中，为了获得较好的性能，必须包含数据本身固有的关系模式，但是手工编写这些规则费时费力俄而且不可能编写全部规则。而且数据中存在一些隐藏的关系模式，这对提供信息抽取性能很有帮助。 二、论文使用的方法和亮点 2.1 亮点 提出一个集成机器学习模型，能够学习上下文关系和关联关系模式来抽取实体之间的关系，并使用线性链条件随机场
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相关文章
- 1. Probabilistic Data Structures for Web Analytics and Data Mining
- 2. ArnetMiner: Extraction and Mining of Academic Social Networks
- 3. Big Data Analysis and Mining
- 4. 论文阅读笔记－CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- 5. END-TO-END NAMED ENTITY RECOGNITION AND RELATION EXTRACTION USING PRE-TRAINED LANGUAGE MODELS
- 6. 【论文笔记】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
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- 8. Data Mining: Practical Machine Learning Tools and Techniques
- 9. 论文学习12-Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data（CRF
- 10. 论文笔记：Securing Data With Blockchain and AI