GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

SciCN:A Scientific Dataset for Chinese Named Entity Recognition

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://github.com/yangjingla/SciCN.git.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

On the Rule of Law Path for Inclusive Education to Empower Education Assistance for Persons with Disabilities

Inclusive education is the mainstream of developing education for persons with disabilities worldwide.It advocates the recognition and protection of the right of persons with disabilities to receive inclusive education in mainstream schools.From the perspective of inclusive education,the educational assistance system for persons with disabilities represents a theoretical innovation in traditional educational support methods,playing a crucial role in integrating persons with disabilities into society,reversing their disadvantaged status,and maintaining educational equity.At present,China's legal system for inclusive education assistance for persons with disabilities needs improvement,and faces several obstacles,including conceptual“limited capacity”,“monotonous”subjects,“crowding-out”obstacles and supervision“absence”obstacles.It is urgent to begin with the transformation of the rule of law concept,clarify the legal positioning of multiple responsibility subjects,achieve mutual reinforcement of education law and education aid legislation,establish a supervision system for inclusive education assistance,and improve the legal framework for educational assistance for persons with disabilities.This will ensure that persons with disabilities can successfully realize their right to education,share in the benefits of social development,and ultimately contribute to achieving common prosperity.

A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models

Named Entity Recognition(NER)is crucial for extracting structured information from text.While traditional methods rely on rules,Conditional Random Fields(CRFs),or deep learning,the advent of large-scale Pre-trained Language Models(PLMs)offers new possibilities.PLMs excel at contextual learning,potentially simplifying many natural language processing tasks.However,their application to NER remains underexplored.This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning.We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity.Our experimental results demonstrate the feasibility of this approach,suggesting a promising direction for harnessing PLMs in NER.

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in sedimentology.Firstly,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity extraction.Furthermore,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text.However,only some labeled data for agricultural knowledge graph domain training are available.Furthermore,labeling is costly due to the need for more data openness and standardization.This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction.Knowledge distillation between the target and source data domain is performed,where Bi-LSTM and CRF models are constructed for entity recognition.The experimental result is shown that we only need to label less than one-tenth of the data for model training.Furthermore,the agricultural domain ontology is constructed by BILSTM-CRF named entity recognition model and relationship extraction model.Moreover,there are a total of 13,983 entities and 26,498 relationships built in the neo4j graph database.

A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user privacy.Meanwhile,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly privacysensitive.How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid.Therefore,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements.We decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local part.The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data.The global part is learned across different operation platforms to capture the shared NER knowledge.Its local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global part.Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

Chinese Cyber Threat Intelligence Named Entity Recognition via RoBERTa-wwm-RDCNN-CRF

In recent years,cyber attacks have been intensifying and causing great harm to individuals,companies,and countries.The mining of cyber threat intelligence(CTI)can facilitate intelligence integration and serve well in combating cyber attacks.Named Entity Recognition(NER),as a crucial component of text mining,can structure complex CTI text and aid cybersecurity professionals in effectively countering threats.However,current CTI NER research has mainly focused on studying English CTI.In the limited studies conducted on Chinese text,existing models have shown poor performance.To fully utilize the power of Chinese pre-trained language models(PLMs)and conquer the problem of lengthy infrequent English words mixing in the Chinese CTIs,we propose a residual dilated convolutional neural network(RDCNN)with a conditional random field(CRF)based on a robustly optimized bidirectional encoder representation from transformers pre-training approach with whole word masking(RoBERTa-wwm),abbreviated as RoBERTa-wwm-RDCNN-CRF.We are the first to experiment on the relevant open source dataset and achieve an F1-score of 82.35%,which exceeds the common baseline model bidirectional encoder representation from transformers(BERT)-bidirectional long short-term memory(BiLSTM)-CRF in this field by about 19.52%and exceeds the current state-of-the-art model,BERT-RDCNN-CRF,by about 3.53%.In addition,we conducted an ablation study on the encoder part of the model to verify the effectiveness of the proposed model and an in-depth investigation of the PLMs and encoder part of the model to verify the effectiveness of the proposed model.The RoBERTa-wwm-RDCNN-CRF model,the shared pre-processing,and augmentation methods can serve the subsequent fundamental tasks such as cybersecurity information extraction and knowledge graph construction,contributing to important applications in downstream tasks such as intrusion detection and advanced persistent threat(APT)attack detection.

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.

Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.

Chinese Named Entity Recognition with Character-Level BLSTM and Soft Attention Model

Unlike named entity recognition(NER)for English,the absence of word boundaries reduces the final accuracy for Chinese NER.To avoid accumulated error introduced by word segmentation,a deep model extracting character-level features is carefully built and becomes a basis for a new Chinese NER method,which is proposed in this paper.This method converts the raw text to a character vector sequence,extracts global text features with a bidirectional long short-term memory and extracts local text features with a soft attention model.A linear chain conditional random field is also used to label all the characters with the help of the global and local text features.Experiments based on the Microsoft Research Asia(MSRA)dataset are designed and implemented.Results show that the proposed method has good performance compared to other methods,which proves that the global and local text features extracted have a positive influence on Chinese NER.For more variety in the test domains,a resume dataset from Sina Finance is also used to prove the effectiveness of the proposed method.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Few-Shot Named Entity Recognition with the Integration of Spatial Features

The few-shot named entity recognition(NER)task aims to train a robust model in the source domain and transfer it to the target domain with very few annotated data.Currently,some approaches rely on the prototypical network for NER.However,these approaches often overlook the spatial relations in the span boundary matrix because entity words tend to depend more on adjacent words.We propose using a multidimensional convolution module to address this limitation to capture short-distance spatial dependencies.Additionally,we uti-lize an improved prototypical network and assign different weights to different samples that belong to the same class,thereby enhancing the performance of the few-shot NER task.Further experimental analysis demonstrates that our approach has significantly improved over baseline models across multiple datasets.

Early identification of scientific breakthroughs through outlier analysis based on research entities

Purpose:To address the“anomalies”that occur when scientific breakthroughs emerge,this study focuses on identifying early signs and nascent stages of breakthrough innovations from the perspective of outliers,aiming to achieve early identification of scientific breakthroughs in papers.Design/methodology/approach:This study utilizes semantic technology to extract research entities from the titles and abstracts of papers to represent each paper’s research content.Outlier detection methods are then employed to measure and analyze the anomalies in breakthrough papers during their early stages.The development and evolution process are traced using literature time tags.Finally,a case study is conducted using the key publications of the 2021 Nobel Prize laureates in Physiology or Medicine.Findings:Through manual analysis of all identified outlier papers,the effectiveness of the proposed method for early identifying potential scientific breakthroughs is verified.Research limitations:The study’s applicability has only been empirically tested in the biomedical field.More data from various fields are needed to validate the robustness and generalizability of the method.Practical implications:This study provides a valuable supplement to current methods for early identification of scientific breakthroughs,effectively supporting technological intelligence decision-making and services.Originality/value:The study introduces a novel approach to early identification of scientific breakthroughs by leveraging outlier analysis of research entities,offering a more sensitive,precise,and fine-grained alternative method compared to traditional citation-based evaluations,which enhances the ability to identify nascent breakthrough innovations.

SHEL:a semantically enhanced hardware-friendly entity linking method

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

Optimization of Rural Governance Structure under the Development of New Agricultural Management Entities

The positive impact of the development of new agricultural business entities and their institutional systems on the optimization of rural governance structures can be examined from various perspectives,including the state,market,rural society,urban-rural relations,and grassroots governance structure in rural communities.The development of these new agricultural business entities has not only redefined but also restructured the power distribution and governance patterns in rural developing countries,markets,and villages.The rural social order has evolved into a ternary mutual structure governance pattern,often referred to as the“state market rural”model.This transformation has prompted adjustments in the national economic and social policy structure and management systems at both macro and micro levels.It has led to the reshaping of power dynamics,benefit distribution,and governance structures in both urban and rural areas,resulting in significant changes to the economic and social fabric of rural regions.Furthermore,the grassroots governance structure in rural society,characterized by“township governance and village governance,”is undergoing continuous development and improvement.This transition is marked by a shift towards a collaborative governance structure that encourages diverse participation.Building upon the aforementioned optimizations,the rural governance structure now exhibits new characteristics.These include a more extensive and diverse range of rural governance mechanisms,increased openness in governance processes,and a heightened synergy among various governance mechanisms.This dynamic evolution reflects a richer,more diverse,and more open approach to rural governance,fostering a stronger collaborative effort in the pursuit of effective governance.