Search Results (286)

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. Full article

In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields. Full article

Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing to errors of unrecognition and misrecognition. Research in named entity recognition predominantly focuses on English, which poses challenges for non-English languages. By specifying procedures for the development of Korean-based tag sets, data collection, and preprocessing, we formulated directions on the application of entity recognition research to non-English languages. Such research could significantly benefit artificial intelligence (AI)-based natural language processing globally. We developed a personal information tag set comprising 33 items and established guidelines for dataset creation, later converting it into JSON format for AI learning. State-of-the-art AI models, BERT and ELECTRA, were employed to implement and evaluate the named entity recognition (NER) model, which achieved an 0.943 F1-score and outperformed conventional recognizers in detecting personal information. This advancement suggests that the proposed NER model can effectively prevent personal information leakage in systems processing interactive text data, marking a significant stride in safeguarding privacy across digital platforms. Full article

Regarding the existing models for feature extraction of complex similar entities, there are problems in the utilization of relative position information and the ability of key feature extraction. The distinctiveness of Chinese named entity recognition compared to English lies in the absence of space delimiters, significant polysemy and homonymy of characters, diverse and common names, and a greater reliance on complex contextual and linguistic structures. An entity recognition method based on DeBERTa-Attention-BiLSTM-CRF (DABC) is proposed. Firstly, the feature extraction capability of the DeBERTa model is utilized to extract the data features; then, the attention mechanism is introduced to further enhance the extracted features; finally, BiLSTM is utilized to further capture the long-distance dependencies in the text and obtain the predicted sequences through the CRF layer, and then the entities in the text are identified. The proposed model is applied to the dataset for validation. The experiments show that the precision (P) of the proposed DABC model on the dataset reaches 88.167%, the recall (R) reaches 83.121%, and the F1 value reaches 85.024%. Compared with other models, the F1 value improves by 3∼5%, and the superiority of the model is verified. In the future, it can be extended and applied to recognize complex entities in more fields. Full article

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. Full article

This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages. Full article

Before starting any construction work, providing workers with awareness about past similar accident cases is effective in preventing mishaps. Based on construction accident reports, this study developed two models to identify past accidents at sites with similar site information. The site information includes 16 parameters, such as type of work, type of accident, the work in which the accident occurred, weather conditions, contract conditions, type of work, etc. The first model, the site information classification model, uses named entity recognition tasks to classify site information, which is extracted from accident reports. The second model, the similar-site accident retrieval model, which finds the most similar accidents that occurred in the past from input site information, uses a semantic textual similarity task to match the classified information with it. A total of 17,707 accident reports from South Korean construction sites were found; these models were trained to use Korean Language Understanding Evaluation–Bidirectional Encoder Representations from Transformers (KLUE-BERT) for processing. The first model achieved an average accuracy of 0.928, and the second model was precisely matched, with a mean cosine similarity score exceeding 0.90. These models could identify and provide workers with similar past accidents, enabling proactive safety measures, such as site-specific hazard identification and worker education, thereby allowing recognition of construction safety risks before starting work. By integrating site information with historical data, the models offer an effective approach to improving construction safety. Full article

With the continuous advancement of information technology in the agricultural field, a large amount of unstructured agricultural textual information has been generated. This information is crucial for supporting the development of smart agriculture, making the application of named entity recognition in the agricultural field more urgent. In order to enhance the accuracy of agricultural entity recognition, this study utilizes the pre-trained BERT-wwm model for word embedding into the text. Additionally, a channel attention mechanism (CA) is introduced in the BILSTM-CRF downstream feature extraction network to comprehensively capture the contextual features of the text. Experimental results demonstrate that the proposed method significantly improves the performance of named entity recognition, with increased accuracy, recall, and F1 value. The successful implementation of this method provides reliable support for downstream tasks such as agricultural knowledge graph construction and question and answer systems and establishes a foundation for better understanding and utilization of agricultural textual information. Full article

The fifth edition of the World Health Organization (WHO) classification for urogenital tumors, released in 2022, introduces some novelties in the chapter on renal epithelial tumors compared to the previous 2016 classification. Significant changes include the recognition of new disease entities and adjustments in the nomenclature for certain pathologies. Notably, each tumor entity now includes minimum essential and desirable criteria for reliable diagnosis. This classification highlights the importance of biological and molecular characterization alongside traditional cytological and architectural features. In this view, immunophenotyping through immunohistochemistry (IHC) plays a crucial role in bridging morphology and genetics. This article aims to present and discuss the role of key immunohistochemical markers that support the diagnosis of new entities recognized in the WHO classification, focusing on critical topics associated with single markers, in the context of specific tumors, such as the clear cell capillary renal cell tumor (CCPRCT), eosinophilic solid and cystic renal cell carcinoma (ESC-RCC), and so-called “other oncocytic tumors”, namely the eosinophilic vacuolated tumor (EVT) and low-grade oncocytic tumor (LOT). Their distinctive characteristics and immunophenotypic profiles, along with insights regarding diagnostic challenges and the differential diagnosis of these tumors, are provided. This state-of-the-art review offers valuable insights in biomarkers associated with novel renal tumors, as well as a tool to implement diagnostic strategies in routine practice. Full article

In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments. Full article

Named entity recognition (NER) is a critical subtask in natural language processing. It is particularly valuable to gain a deeper understanding of entity boundaries and entity types when addressing the NER problem. Most previous sequential labeling models are task-specific, while recent years have witnessed the rise of generative models due to the advantage of tackling NER tasks in the encoder–decoder framework. Despite achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. In this paper, a multiple attention framework is proposed which introduces the attention of entity-type embedding and word–word relation into the named entity recognition task. To improve the accuracy of entity-type mapping, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information input to the model via the encoder’s self-attention. To enhance the contextual information, we take the entity types as part of the input. Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross-attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word–word relations and extract the corresponding embedding into the cross-attention mechanism. Through word–word relation information, the method can learn and understand more entity boundary information, thereby improving its entity recognition accuracy. We performed experiments on extensive NER benchmarks, including four flat and two long entity benchmarks. Our approach significantly improves or performs similarly to the best generative NER models. The experimental results demonstrate that our method can substantially enhance the capabilities of generative NER models. Full article

With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students. Full article

In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity. Full article

The goal of multimodal named entity recognition (MNER) is to detect entity spans in given image–text pairs and classify them into corresponding entity types. Despite the success of existing works that leverage cross-modal attention mechanisms to integrate textual and visual representations, we observe three key issues. Firstly, models are prone to misguidance when fusing unrelated text and images. Secondly, most existing visual features are not enhanced or filtered. Finally, due to the independent encoding strategies employed for text and images, a noticeable semantic gap exists between them. To address these challenges, we propose a framework called visual clue guidance and consistency matching (GMF). To tackle the first issue, we introduce a visual clue guidance (VCG) module designed to hierarchically extract visual information from multiple scales. This information is utilized as an injectable visual clue guidance sequence to steer text representations for error-insensitive prediction decisions. Furthermore, by incorporating a cross-scale attention (CSA) module, we successfully mitigate interference across scales, enhancing the image’s capability to capture details. To address the third issue of semantic disparity between text and images, we employ a consistency matching (CM) module based on the idea of multimodal contrastive learning, facilitating the collaborative learning of multimodal data. To validate the effectiveness of our proposed framework, we conducted comprehensive experimental studies, including extensive comparative experiments, ablation studies, and case studies, on two widely used benchmark datasets, demonstrating the efficacy of the framework. Full article

Complex systems science (CSS) and community-based research (CBR) have emerged over the past 50 years as complementary disciplines. However, there is a gap in understanding what has driven the recent proliferation of integrating these two disciplines to study complex and relevant issues. In this review, we report on the results of a scoping review of articles that utilized both disciplines. After two levels of reviewing articles using DistillerSR, a web-based platform designed to streamline and facilitate the process of conducting systematic reviews, we used two forms of natural language processing to extract data. We developed a novel named entity recognition model to extract descriptive information from the corpus of articles. We also conducted dynamic topic modeling to deductively examine in tandem the development of CSS and CBR and to inductively discover the specific topics that may be driving their use in research and practice. We find that among the CSS and CBR papers, CBR topic frequency has grown at a faster pace than CSS, with CBR using CSS concepts and techniques more often. Four topics that may be driving this trend are collaboration within social systems, business management, food and land use and knowledge, and water shed management. We conclude by discussing the implications of this work for researchers and practitioners who are interested in studying and solving complex social, economic, and health-related issues. Full article