GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Show simple item record

dc.contributor.author Qinjun Qiu
dc.contributor.author Zhong Xie
dc.contributor.author Liang Wu
dc.contributor.author Liufeng Tao
dc.date.accessioned 2023-09-11T07:56:04Z
dc.date.available 2023-09-11T07:56:04Z
dc.date.issued 2019
dc.identifier.citation Earth and Space Science, 2019, 6, 931–946 ru_RU
dc.identifier.uri https://repository.geologyscience.ru/handle/123456789/41602
dc.description.abstract A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NERmodels and techniques aremainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities. ru_RU
dc.language.iso en ru_RU
dc.subject geological named entities ru_RU
dc.subject deep learning ru_RU
dc.title GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning ru_RU
dc.type Article ru_RU
dc.identifier.doi 10.1029/2019EA000610


Files in this item

This item appears in the following Collection(s)

Show simple item record