Research of Extraction on Petroleum Unstructured Information Based on Named Entity Recognition

doi:10.11885/j.issn.1674-5086.2020.05.12.01

Abstract

Abstract: With the acceleration of the construction of "intelligent oilfield", it is of great significance to build an intelligent analysis system for mass oil data. However, as a result of the dynamic text data generated in oilfield production process is often of unstructured and various types. Extracting the crucial information for analysis becomes a popular area of research, and information extraction needs high-quality entities to support. In this paper, we propose an unstructured text information extraction method based on NER (Named Entity Recognition) according to the particular problem. Feature extraction of oil corpus is carried out by Bidirectional Long Short-Term Memory (Bi-LSTM) network model, and combines Conditional Random Field (CRF) as classifier. Bi-LSTM+CRF method is used to construct a high-precision NER model to extract named entities from unstructured texts in petroleum industry. The experimental results on the text data set of well workover treatment show that this method has a higher precision and recall rate than other state-of-art methods.

Key words: NER, Bi-LSTM+CRF, information extraction, unstructured text

CLC Number:

TE319

ZHONG Yuan, LIU Xiaorong, WANG Jie, CHEN Yan, ZHANG Tai. Research of Extraction on Petroleum Unstructured Information Based on Named Entity Recognition[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 165-173.

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URL: http://journal15.magtechjournal.com/Jwk_xnzk/EN/10.11885/j.issn.1674-5086.2020.05.12.01

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