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

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 NER models and techniques are mainly 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.

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

“…In the domain of geosciences, various systems and applications, such as mineral exploration (Holden et al., 2019; Shi et al., 2018), paleontological studies (Peters et al., 2014, 2017; Wang et al., 2018), and geological text mining and application in Chinese (Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Wang et al., 2018), have been developed and constructed.…”

“…Many types of entities are also mentioned in various geological reports, which include not only significant locations, but also rocks, minerals, to stratigraphic units, locations, geological timescales (Fan et al., 2020; Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Wang et al., 2021; Wu et al., 2017). Named entity recognition (NER) has received much attention from the academic field and industry for many years (Nieh et al., 2021; Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Zhou et al., 2021). The basis of geological domain knowledge graph construction is named entity recognition, that is, the recognition of specific categories of proper noun entities from unprocessed geological domain texts, and its accuracy directly affects the results of multiple natural language processing techniques in the geological domain (Ma et al., 2020, 2021; Wang et al., 2018).…”

“…They cover a variety of topics about geological survey, ranging from ore/deposits, mineral, remote sensing, to geology, hydrology. Many types of entities are also mentioned in various geological reports, which include not only significant locations, but also rocks, minerals, to stratigraphic units, locations, geological timescales (Fan et al., 2020; Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Wang et al., 2021; Wu et al., 2017). Named entity recognition (NER) has received much attention from the academic field and industry for many years (Nieh et al., 2021; Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Zhou et al., 2021).…”

Chinese Word Segmentation Based on Self‐Learning Model and Geological Knowledge for the Geoscience Domain

“…It means an urgent need to improve information extraction, knowledge mining, and knowledge association in heterogeneous geological data (Li & Shao, 2009). Recent studies have shown that the performance of downstream tasks for text mining, such as part‐of‐speech tagging, retrieve text from images (Shao et al., 2020; Zhou et al., 2017) and named entity recognition (Ma et al., 2018; Qiu et al., 2018, 2019; L. Wu et al., 2017), strongly depends on high precision geological word segmentation methods. This is because all of these downstream tasks, beside the extraction of multi‐level and multi‐dimensional image features required in retrieving text from images, require the system to have a good understanding of the text, which is the cornerstone of Chinese word segmentation (CWS).…”

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

“…The textual content from studies is recorded and presented in a variety of types, such as journal papers, technical documents/reports, and monographs and books (Huang et al, ). Many national geological survey agencies focus on handling georeferenced quantitative data/information, including rock structures, geochemical anomalies, satellite imagery, and geophysical surveys (Qiu et al, ; Wu et al, ). As a key component of open and published data, geoscience documents/reports often contain potential and valuable information for further research (Wang et al, ).…”