論文筆記5：Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns
使用條件隨機場抽取信息，抽取實體間的二元關係 一、論文要解決的問題 在信息抽取系統中，爲了獲得較好的性能，必須包含數據本身固有的關係模式，但是手工編寫這些規則費時費力俄而且不可能編寫全部規則。而且數據中存在一些隱藏的關係模式，這對提供信息抽取性能很有幫助。 二、論文使用的方法和亮點 2.1 亮點 提出一個集成機器學習模型，能夠學習上下文關係和關聯關係模式來抽取實體之間的關係，並使用線性鏈條件隨機場
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相關文章
- 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. 論文閱讀筆記－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. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 7. 「論文翻譯」Predicting potential DDI by integrating chemical, biological, phenotypic and network data
- 8. Data Mining: Practical Machine Learning Tools and Techniques
- 9. 論文學習12-Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data（CRF
- 10. 論文筆記：Securing Data With Blockchain and AI