Named Entity Recognition Research Articles

On improving conversational interfaces in educational systems

Conversational Intelligent Tutoring Systems (CITS) have drawn increasing interest in education because of their capacity to tailor learning experiences, improve user engagement, and contribute to the effective transfer of knowledge. Conversational agents employ advanced natural language techniques to engage in a convincing human-like tutorial conversation. In solving math word problems, a significant challenge arises in enabling the system to understand user utterances and accurately map extracted entities to the essential problem quantities required for problem-solving, despite the inherent ambiguity of human natural language. In this study, we propose two possible approaches to enhance the performance of a particular CITS designed to teach learners to solve arithmetic–algebraic word problems. Firstly, we propose an ensemble approach to intent classification and entity extraction, which combines the predictions made by two distinct individual models that use constraints defined by human experts. This approach leverages the intertwined nature of the intents and entities to yield a comprehensive understanding of the user’s utterance, ultimately aiming to enhance semantic accuracy. Secondly, we introduce an adapted Term Frequency-Inverse Document Frequency technique to associate entities with problem quantity descriptions. The evaluation was conducted on the AWPS and MATH-HINTS datasets, containing conversational data and a collection of arithmetical and algebraic math problems, respectively. The results demonstrate that the proposed ensemble approach outperforms individual models, and the proposed method for entity–quantity matching surpasses the performance of typical text semantic embedding models.

ICKA: An instruction construction and Knowledge Alignment framework for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) aims to identify entities of predefined types in text by leveraging information from multiple modalities, most notably textual and visual information. Most efforts concentrate on improving cross-modality attention mechanisms to facilitate guidance between modalities. However, they still suffer from certain limitations: (1) it is difficult to establish a unified representation to bridge the semantic gap among different modalities; (2) mining the implicit relationships between text and image is crucial yet challenging. In this paper, we propose an Instruction Construction and Knowledge Alignment Framework for MNER named ICKA to address these issues. Specifically, we first employ a multi-head cross-modal attention mechanism to obtain the cross-modal fusion representation by fusing features from text–image pairs. Then, we integrate external knowledge from the pre-trained vision-language model (VLM) to facilitate semantic alignment between text and image and obtain inter-modality connections. Next, we construct the multimodal instruction that consists of the modal features and uses the inter-modality connections as a bridge between them. We then integrate the instruction into the language model to effectively incorporate multimodal knowledge. Finally, we perform sequence labeling using a Conditional Random Fields (CRF) decoder with a gating mechanism. The proposed method achieves F1 scores of 75.42% on the Twitter2015 dataset and 87.12% on the Twitter2017 dataset, demonstrating the competitiveness of our method.

CLESR: Context-Based Label Knowledge Enhanced Span Recognition for Named Entity Recognition

Named entity recognition (NER) stands as a pivotal task in natural language processing, bearing profound implications for diverse downstream applications. Recent scholarship underscores the substantial performance gains attainable by integrating label knowledge into token representations, effectively treating named entity recognition as a machine reading comprehension task. Nevertheless, extant methodologies often inadequately leverage the potential of label knowledge. In response to this limitation, we introduce the Context-based Label Knowledge Enhanced Span Recognition (CLESR) architecture, designed to augment label knowledge through the assimilation of contextual information. We formulate an annotation paradigm tailored to nested scenarios, concurrently training external context-based label knowledge using conventional word association learning algorithms. The ensuing context-based label knowledge is seamlessly integrated into the model via a dedicated label attention module, thereby fortifying label learning capabilities during training. To adeptly manage both flat and nested entities, we implement a global pointer as our decoding strategy, enabling direct predictions of the positions and corresponding categories of named entities. Rigorous experimentation across six widely recognized benchmarks substantiates the efficacy of CLESR in both flat and nested tasks. Impressively, our model achieves performance enhancements surpassing those of the baseline BERT-MRC model, with gains of + 0.29%, + 0.77%, + 0.39%, + 1.9%, and + 0.47% on English CoNLL2003, English OntoNotes 4.0, Chinese MSRA, English ACE2004, and English ACE2005, respectively.

Multimodal heterogeneous graph entity-level fusion for named entity recognition with multi-granularity visual guidance

Multimodal heterogeneous graph entity-level fusion for named entity recognition with multi-granularity visual guidance

MedNER: Enhanced Named Entity Recognition in Medical Corpus via Optimized Balanced and Deep Active Learning

Ever-growing electronic medical corpora provide unprecedented opportunities for researchers to analyze patient conditions and drug effects. Meanwhile, severe challenges emerged in the large-scale electronic medical records process phase. Primarily, emerging words for medical terms, including informal descriptions, are difficult to recognize. Moreover, although deep models can help in entity extraction on medical texts, it requires large-scale labels which are time-intensive to obtain and not always available in the medical domain. However, when encountering a situation where massive unseen concepts appear, or labeled data is insufficient, the performance of existing algorithms will suffer an intolerable decline. In this paper, we propose a balanced and deep active learning framework ( MedNER ) for Named Entity Recognition in the medical corpus to alleviate above problems. Specifically, to describe our selection strategy precisely, we first define the uncertainty of a medical sentence as a labeling loss predicted by a loss-prediction module and define diversity as the least text distance between pairs of sentences in a sample batch computed based on word-morpheme embeddings. Furthermore, aiming to make a trade-off between uncertainty and diversity, we formulate a Distinct-K optimization problem to maximize the slightest uncertainty and diversity of chosen sentences. Finally, we propose a threshold-based approximation selection algorithm, Distinct-K Filter , which selects the most beneficial training samples by balancing diversity and uncertainty. Extensive experimental results on real datasets demonstrate that MedNER significantly outperforms existing approaches.

Research on precise identification and localization methods for static small targets based on multimodal data fusion

This investigation introduces a multimodal fusion-based methodology for the precise detection and accurate localization of static small targets against uniform backgrounds. Small targets often present fewer pixels in imagery, which significantly reduces the detectable features and complicates accurate identification and localization. Our approach integrates visual data from CMOS cameras, distance readings from laser rangefinders, and orientation data from angle sensors. The image processing involves non-local means denoising and Canny edge detection to enhance small target detection. We map two-dimensional images to three-dimensional spaces, combining depth and orientation to precisely determine the target’s coordinates, aiding in three-dimensional localization. Empirical results confirm the method’s high accuracy, with a localization error under 2.5%. Such conclusive evidence highlights the approach’s efficacy, presenting a cost-efficient alternative for the nuanced detection and localization of small entities.

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

Evolution and advancements in deep learning models for Natural Language Processing

This paper provides a comprehensive review of the evolution and advancements in deep learning models for Natural Language Processing (NLP). It explores the transition from statistical models to neural networks, highlighting the paradigm shift towards data-driven methodologies and the implications for NLP tasks. The emergence of neural network architectures, such as Recurrent Neural Networks (RNNs) and transformer-based models like BERT and GPT, has revolutionized language understanding and generation. Furthermore, the integration of deep learning in traditional NLP tasks, such as part-of-speech tagging and named entity recognition, has led to significant improvements in accuracy and efficiency. The paper also discusses the quantitative analysis of deep learning models, including performance metrics, computational efficiency, and mathematical modeling of language tasks. Case studies and applications, including sentiment analysis, machine translation, and automated content generation, exemplify the transformative impact of deep learning in NLP.

LLM-TIKG: Threat intelligence knowledge graph construction utilizing large language model

Open-source threat intelligence is often unstructured and cannot be directly applied to the next detection and defense. By constructing a knowledge graph through open-source threat intelligence, we can better apply this information to intrusion detection. However, the current methods for constructing knowledge graphs face limitations due to the domain-specific attributes of entities and the analysis of lengthy texts, and they require large amounts of labeled data. Furthermore, there is a lack of authoritative open-source annotated threat intelligence datasets, which require significant manual effort. Moreover, it is noteworthy that current research often neglects the textual descriptions of attack behaviors, resulting in the loss of vital information to understand intricate cyber threats. To address these issues, we propose LLM-TIKG that applies the large language model to construct a knowledge graph from unstructured open-source threat intelligence. The few-shot learning capability of GPT is leveraged to achieve data annotation and augmentation, thereby creating the datasets for fine-tuning a smaller language model (7B). Using the fine-tuned model, we perform topic classification on the collected reports, extract entities and relationships, and extract TTPs from the attack description. This process results in the construction of a threat intelligence knowledge graph, enabling automated and universal analysis of textualized threat intelligence. The experimental results demonstrate improved performance in both named entity recognition and TTP classification, achieving the precision of 87.88% and 96.53%, respectively.

Multi-Level Attention with 2D Table-Filling for Joint Entity-Relation Extraction

Joint entity-relation extraction is a fundamental task in the construction of large-scale knowledge graphs. This task relies not only on the semantics of the text span but also on its intricate connections, including classification and structural details that most previous models overlook. In this paper, we propose the incorporation of this information into the learning process. Specifically, we design a novel two-dimensional word-pair tagging method to define the task of entity and relation extraction. This allows type markers to focus on text tokens, gathering information for their corresponding spans. Additionally, we introduce a multi-level attention neural network to enhance its capacity to perceive structure-aware features. Our experiments show that our approach can overcome the limitations of earlier tagging methods and yield more accurate results. We evaluate our model using three different datasets: SciERC, ADE, and CoNLL04. Our model demonstrates competitive performance compared to the state-of-the-art, surpassing other approaches across the majority of evaluated metrics.

Few-shot relational triple extraction with hierarchical prototype optimization

Relational Triple Extraction (RTE) aims to extract relations and entities from unstructured text. Current RTE models using supervised learning require a large amount of labeled data, which presents a challenge for real-world applications. Therefore, the research work on Few-Shot Relational Triple Extraction (FS-RTE) has been proposed. However, the existing work cannot effectively construct accurate prototypes from a small number of samples, and it is difficult to model the dependencies between entities and relations, resulting in poor performance in relational triple extraction. In this paper, we propose a Hierarchical Prototype Optimized FS-RTE method (HPO). In particular, to mitigate prototype bias built on a small number of samples, HPO uses prompt learning to merge the information of relational labels into the text. Then, the entity-level prototypes are constructed using a span encoder to avoid label dependency between entity tokens. Finally, the hierarchical contrastive learning (HCL) method is introduced to improve the metric space between the prototypes of entities and relations, respectively. Experiments conducted on two public datasets show that HPO can significantly outperform previous state-of-the-art methods.

Bi-directional context-aware network for the nested named entity recognition

The Span-based model can effectively capture the complex entity structure in the text, thus becoming the mainstream model for nested named entity recognition (Nested NER) tasks. However, traditional Span-based models decode each entity span independently. They do not consider the semantic connections between spans or the entities’ positional information, which limits their performance. To address these issues, we propose a Bi-Directional Context-Aware Network (Bi-DCAN) for the Nested NER. Specifically, we first design a new span-level semantic relation model. Then, the Bi-DCAN is implemented to capture this semantic relationship. Furthermore, we incorporate Rotary Position Embedding into the bi-affine mechanism to capture the relative positional information between the head and tail tokens, enabling the model to more accurately determine the position of each entity. Experimental results show that compared to the latest model Diffusion-NER, our model reduces 20M parameters and increases the F1 scores by 0.24 and 0.09 on the ACE2005 and GENIA datasets respectively, which proves that our model has an excellent ability to recognise nested entities.

Towards discovery: an end-to-end system for uncovering novel biomedical relations.

Biomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively addressing this task holds the potential to improve knowledge discovery by automating the extraction of relations from research manuscripts. The first track in the BioCreative VIII competition extended the scope of this challenge by introducing the detection of novel relations within the literature. This paper describes that our participation system initially focused on jointly extracting and classifying novel relations between biomedical entities. We then describe our subsequent advancement to an end-to-end model. Specifically, we enhanced our initial system by incorporating it into a cascading pipeline that includes a tagger and linker module. This integration enables the comprehensive extraction of relations and classification of their novelty directly from raw text. Our experiments yielded promising results, and our tagger module managed to attain state-of-the-art named entity recognition performance, with a micro F1-score of 90.24, while our end-to-end system achieved a competitive novelty F1-score of 24.59. The code to run our system is publicly available at https://github.com/ieeta-pt/BioNExt. Database URL: https://github.com/ieeta-pt/BioNExt.

Predictive Model for Extended-Spectrum β-Lactamase-Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study.

Machine learning has advanced medical event prediction, mostly using private data. The public MIMIC-3 (Medical Information Mart for Intensive Care III) data set, which contains detailed data on over 40,000 intensive care unit patients, stands out as it can help develop better models including structured and textual data. This study aimed to build and test a machine learning model using the MIMIC-3 data set to determine the effectiveness of information extracted from electronic medical record text using a named entity recognition, specifically QuickUMLS, for predicting important medical events. Using the prediction of extended-spectrum β-lactamase (ESBL)-producing bacterial infections as an example, this study shows how open data sources and simple technology can be useful for making clinically meaningful predictions. The MIMIC-3 data set, including demographics, vital signs, laboratory results, and textual data, such as discharge summaries, was used. This study specifically targeted patients diagnosed with Klebsiella pneumoniae or Escherichia coli infection. Predictions were based on ESBL-producing bacterial standards and the minimum inhibitory concentration criteria. Both the structured data and extracted patient histories were used as predictors. In total, 2 models, an L1-regularized logistic regression model and a LightGBM model, were evaluated using the receiver operating characteristic area under the curve (ROC-AUC) and the precision-recall curve area under the curve (PR-AUC). Of 46,520 MIMIC-3 patients, 4046 were identified with bacterial cultures, indicating the presence of K pneumoniae or E coli. After excluding patients who lacked discharge summary text, 3614 patients remained. The L1-penalized model, with variables from only the structured data, displayed a ROC-AUC of 0.646 and a PR-AUC of 0.307. The LightGBM model, combining structured and textual data, achieved a ROC-AUC of 0.707 and a PR-AUC of 0.369. Key contributors to the LightGBM model included patient age, duration since hospital admission, and specific medical history such as diabetes. The structured data-based model showed improved performance compared to the reference models. Performance was further improved when textual medical history was included. Compared to other models predicting drug-resistant bacteria, the results of this study ranked in the middle. Some misidentifications, potentially due to the limitations of QuickUMLS, may have affected the accuracy of the model. This study successfully developed a predictive model for ESBL-producing bacterial infections using the MIMIC-3 data set, yielding results consistent with existing literature. This model stands out for its transparency and reliance on open data and open-named entity recognition technology. The performance of the model was enhanced using textual information. With advancements in natural language processing tools such as BERT and GPT, the extraction of medical data from text holds substantial potential for future model optimization.

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

BackgroundAs global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.MethodsIn this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.ResultsThe use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.ConclusionThe diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records

Background: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient safety and treatment outcomes.Methods: We explore the influence of domain shift between a set of dummy clinical notes and a real-world hospital corpus of Japanese clinical notes of breast cancer treatment when extracting ADEs from free text. We annotated a subset of the hospital dataset and used it to fine-tune a Named Entity Recognition (NER) model, initially trained with the set of dummy documents. We used increasing amounts of the annotated data and evaluated the impact on the model's performance. Additionally, we examined the extracted information to identify combinations of drugs that are likely to cause ADEs.Results: We show that domain adaptation can significantly improve model performance in the new domain, as by feeding a small subset of 100 documents for the fine-tuning process we saw a 40% improvement in model performance. However, we also noticed diminishing returns when fine-tuning the model with a larger dataset. For instance, by feeding eight times more data, we only saw further 18% improvement in extraction performance.Conclusion: While variations in writing style and vocabulary in clinical corpora can significantly impact the quality of NER results. We show that domain adaptation can be of great aid in mitigating these discrepancies and achieving better performance. Yet, while providing in-domain data to a model helps, there are diminishing returns when fine-tuning with large amounts of data.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Modified R-BERT with global semantic information for relation classification task

The objective of the relation classification task is to extract relations between entities. Recent studies have found that R-BERT (Wu and He, 2019) based on pre-trained BERT (Devlin et al., 2019) acquires extremely good results in the relation classification task. However, this method does not take into account the semantic differences between different kinds of entities and global semantic information either. In this paper, we set two different fully connected layers to take into account the semantic difference between subject and object entities. Besides, we build a new module named Concat Module to fully fuse the semantic information among the subject entity vector, object entity vector, and the whole sample sentence representation vector. In addition, we apply the average pooling to acquire a better entity representation of each entity and add the activation operation with a new fully connected layer after our Concat Module. Modifying R-BERT, we propose a new model named BERT with Global Semantic Information (GSR-BERT) for relation classification tasks. We use our approach on two datasets: the SemEval-2010 Task 8 dataset and the Chinese character relationship classification dataset. Our approach achieves a significant improvement over the two datasets. It means that our approach enjoys transferability across different datasets. Furthermore, we prove that these policies we used in our approach also enjoy applicability to named entity recognition task.

Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers. We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves. The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

Automated Processing Method for Chinese NOTAMs Based on Knowledge Graph

Notice to airmen (NOTAMs) constitutes a vital element in civil aviation operational intelligence. Historically, the processing of these notices has been manual. However, with the significant increase in the number of NOTAMs, issues including low efficiency, time-consuming processes, and high error rates associated with manual processing have become apparent. To address these challenges, we propose an enhanced approach utilizing the Bi-GRU-CRF-Attention model, based on a dataset of 105,797 NOTAMs collected from the Intelligence Center between September 2020 and April 2023. In this methodology, we employ preprocessing techniques to train the model using processed NOTAMs. Subsequently, the trained model is utilized for named entity recognition, identifying entities within the notices, such as status, facilities, and reasons and segmenting sentences into words. Following this, an advanced BERT-DPCNN method is employed to classify the identified entities, yielding triplets comprising NOTAM entities, their categories, and corresponding processing methods. By integrating rule-based approaches, we configure a NOTAM knowledge graph using neo4j. This process establishes an automated NOTAM processing system. This system can autonomously determine the category of a NOTAM upon reception and utilize the Cypher language to query for the appropriate processing method.