Named Entity Recognition Research Articles

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

Governance in multi-system transitions: A new methodological approach for actor involvement in policy making processes

Multi-system interactions associated with the decarbonisation of energy and mobility systems represent a complex phenomenon in the acceleration phase of net-zero transitions. In this paper, we present a novel methodological approach to examine actor involvement in the governance of multi-system transitions, with a focus on the UK's net-zero energy-mobility transitions from 2008 to 2021. Utilising Named Entity Recognition (NER), a natural language processing technique, we systematically map actors and their interactions within policy consultations and how these have changed over time. Our analysis differentiates between single-system and multi-system policy making processes; identifies weak and strong links among actors as two types of multi-system interactions; categorises actors into business, policy, academia, and society groups; and examines the evolution of engagement across multiple governance levels. Our findings indicate an increasing trend of multi-system interactions, suggesting the UK's progression towards the acceleration phase of net-zero transitions. Our analysis further reveals the predominance of policy actors, particularly from the national level, in governing such multi-system transitions processes, followed by business actors. Despite some limitations, our approach offers a scalable method for analysing large volumes of text, providing valuable insights into the governance dynamics of multi-system transitions. We conclude with implications for policy making and offer suggestions for future research, emphasising the importance of understanding actor involvement and political contestations around net-zero trajectories for ensuring the achievement of sustainability goals.

A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.

With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. The identification of entities in texts, such as diseases or genes, and their normalization, i.e. grounding them in knowledge base, are crucial steps in any BTM pipeline to enable information aggregation from multiple documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied "in the wild," i.e. on application-dependent text collections from moderately to extremely different from those used for training, varying, e.g. in focus, genre or text type. This raises the question whether the reported performance, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for entity recognition and normalization, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on predefined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora covering four entity types. Our results present a mixed picture and show that cross-corpus performance is significantly lower than the in-corpus performance. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator Central. Our results indicate that users of BTM tools should expect a lower performance than the original published one when applying tools in "the wild" and show that further research is necessary for more robust BTM tools. All our models are integrated into the Natural Language Processing (NLP) framework flair: https://github.com/flairNLP/flair. Code to reproduce our results is available at: https://github.com/hu-ner/hunflair2-experiments.

End-to-end entity extraction from OCRed texts using summarization models

End-to-end entity extraction from OCRed texts using summarization models

RAF-AG: Report analysis framework for attack path generation

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances.This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

SWENER-1800

Named entity recognition (NER) is the process of automatically identifying persons, places, organisations and other name-like entities in text, in order to perform natural language processing tasks such as automatic extraction of metadata from text, anonymisation/pseudonymisation of sensitive personal data, or as a preprocessing step for linking different terms describing the same entity to a single reference. While NER is a mature language technology, it is generally lacking for historical language varieties. We describe our work on compiling SWENER-1800, a large (half a million words) reference corpus of historical Swedish texts, covering the time period from the first half of the 18th century until about 1900, and manually annotating it with named entity types identified as significant for this time period, as well as with sentence boundaries, notoriously difficult to recognise automatically in historical text. This corpus can then be used to train and evaluate NER systems and sentence segmenters for historical Swedish text. An additional concrete contribution from this work is a manual for annotation of named entities in historical Swedish.

MAKG: A maritime accident knowledge graph for intelligent accident analysis and management

With the increasing frequency of human activities at sea, maritime accidents are occurring more often. Analyzing and mining maritime accident cases can help uncover the causal mechanisms behind these incidents, thereby enhancing maritime safety. As an emerging technology for knowledge management and mining, knowledge graphs offer significant support for the storage, reasoning, and decision-making processes related to maritime accidents.In this study, we established a knowledge graph construction and application framework for maritime accidents to facilitates the extraction and management of maritime knowledge from unstructured texts. First, 581 accident reports released by the China Maritime Safety Administration over the past decade (2014–2023) were used as the data basis for analysis and construction of the maritime accident ontology structure using the seven-step method, which comprises 8 entity types, 8 relationship types, and 18 attribute entity types. Second, We proposed MBERT-BiLSTM-CRF-SF, a named entity recognition model based on domain pretraining and self-training, to reduce graph construction costs. This model achieved state-of-the-art performance in the maritime domain, with an F1 score of 0.910 ± 0.006, which is about 5% higher than the mainstream model. In addition, we proposed an entity alignment method based on font and semantics to refine knowledge further. On the basis of the proposed method, we constructed a large, high-quality maritime accident knowledge graph (MAKG) system that contains 16,099 entities and 20,809 relationship instances. Finally, we reduced the complexity of applying knowledge graphs by integrating the CRISPE prompt learning framework of the large language model, and experiments on graph traversal, pattern recognition, and aggregation analysis were conducted to assess the quality of MAKG. Results demonstrate that MAKG can effectively enhance the efficiency of querying and reasoning about maritime accident information, thus providing significant support for the prevention and management of maritime accidents.

From Ads to Action: The Influence of Interest Group Supreme Court Confirmation Advertisements on Political Engagement

Interest groups capitalize on the opportunity to influence salient U.S. Supreme Court confirmations, while pursuing organizational and policy goals and hoping to mobilize the public. We test the opportunities and limits of ad influence on political mobilization specifically looking at calls to action (i.e., donations, contacting senators, and social media engagement). By analyzing responses to real interest group-produced video ads from Amy Coney Barrett’s and Ketanji Brown Jackson’s nominations, we discover ads can potentially influence individuals’ political behavior online and spur offline political engagement. Specifically, we find name recognition, partisan congruence, and positive tone positively influence respondent engagement in calls to action. We find ad format in terms of forced exposure and negative tone decrease engagement likelihood. Our study contributes novel insights into the role of online advertising in shaping political behavior around nominations in the digital age.

An analysis of FRE @ BC8 SympTEMIST track: named entity recognition.

This paper is a more in-depth analysis of the approaches used in our submission (Martínez A, García-Santa N. (2023) FRE @ BC8 SympTEMIST track: Named Entity Recognition Zenodo.) to the 'SympTEMIST' Named Entity Recognition (NER) shared subtask at 'BioCreative 2023'. We participated on the challenge submitting two systems based on a RoBERTa architecture LLM trained on Spanish-language clinical data available at 'HuggingFace' model repository. Before choosing the systems that would be submitted, we tried different combinations of the techniques described here: Conditional Random Fields and Byte-Pair Encoding dropout. In the second system we also included Sub-Subword feature based embeddings (SSW). The test set used in the challenge has now been released (López SL, Sánchez LG, Farré E etal. (2024) SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction. Zenodo), allowing us to analyze more in depth our methods, as well as measuring the impact of introducing data from CARMEN-I (Lima-López S, Farré-Maduell E, Krallinger M. (2023) CARMEN-I: Clinical Entities Annotation Guidelines in Spanish. Zenodo) corpus. Our experiments show the moderate effect of using the Sub-Subword feature based embeddings and the impact of including the symptom NER data from the CARMEN-I dataset. Database URL: https://physionet.org/content/carmen-i/1.0/.

Impedimetric Gram-Positive Bacteria Biosensor Using Vancomycin-Coated Silica Nanoparticles with a Gold Nanocluster-Deposited Electrode.

We introduce a swift, label-free electrochemical biosensor designed for the precise on-site detection of Gram-positive bacteria via electrochemical impedance spectroscopy. The biosensor was prepared by electroplating the electrode surface with gold nanoclusters (AuNCs) on the gold-interdigitated wave-shaped electrode with a printed circuit board (Au-PCB) electrode, which plays a role in cost-effective and promising lab-on-a-chip microsystems and integrated biosensing systems. This was followed by the application of silica nanoparticle-modified vancomycin (SiNPs-VAN) that binds to Gram-positive bacteria and facilitates their detection on the AuNC-coated surface. The biosensor demonstrated remarkable sensitivity and specificity. It could detect as few as 102 colony-forming units (CFU)/mL of Staphylococcus aureus, 101 CFU/mL of Bacillus cereus, and 102 CFU/mL of Micrococcus luteus within 20 min. Additionally, SiNPs-VAN is also known for its high stability, low cost, and ease of preparation. It is effective in identifying Gram-positive bacteria in water samples across a concentration range of 102-105 CFU/mL and shows selective identification of Gram-positive bacteria with minimal interference from Gram-negative bacteria like Escherichia coli. The ability of the biosensor to quantify Gram-positive bacteria aligns well with the results obtained from the quantitative real-time polymerase chain reaction (qRT-PCR). These findings highlight the potential of electrochemical biosensors for the detection of pathogens and other biological entities, marking a significant advancement in this field.

Class incremental named entity recognition without forgetting

Class incremental named entity recognition without forgetting

Joint multimodal entity-relation extraction based on temporal enhancement and similarity-gated attention

Joint Multimodal Entity and Relation Extraction (JMERE), which needs to combine complex image information to extract entity-relation quintuples from text sequences, posts higher requirements of the model’s multimodal feature fusion and selection capabilities. With the advancement of large pre-trained language models, existing studies focus on improving the feature alignments between textual and visual modalities. However, there remains a noticeable gap in capturing the temporal information present in textual sequences. In addition, these methods exhibit a certain deficiency in distinguishing irrelevant images when integrating image and text features, making them susceptible to interference from image information unrelated to the text. To address these challenges, we propose a temporally enhanced and similarity-gated attention network (TESGA) for joint multimodal entity relation extraction. Specifically, we first incorporate an LSTM-based Text Temporal Enhancement module to enhance the model’s ability to capture temporal information from the text. Next, we introduce a Text-Image Similarity-Gated Attention mechanism, which controls the degree of incorporating image information based on the consistency between image and text features. Subsequently, We design the entity and relation prediction module using a form-filling approach based on entity and relation types, and conduct prediction of entity-relation quintuples. Notably, apart from the JMERE task, our approach can also be applied to other tasks involving text-visual enhancement, such as Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE). To demonstrate the effectiveness of our approach, our model is extensively experimented on three benchmark datasets and achieves state-of-the-art performance. Our code will be available upon paper acceptance.11https://github.com/vacuum-cup/TESGA.

Named Entity Recognition for Crop Diseases and Pests Based on Gated Fusion Unit and Manhattan Attention

Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and enhances it using bidirectional long short-term memory (BiLSTM) to extract features from the context. It uses a gated unit to perform weighted fusion between the outputs of RoBERTa and BiLSTM, thereby enriching the information flow. The fused output is then fed into a novel Manhattan attention mechanism to capture the long-range dependencies. The global optimum tagging sequence is obtained using the conditional random fields layer. To enhance the model’s robustness, we incorporate adversarial training using the fast gradient method. This introduces adversarial examples, allowing the model to learn more disturbance-resistant feature representations, thereby improving its performance against unknown inputs. GatedMan achieved F1 scores of 93.73%, 94.13%, 93.98%, and 96.52% on the AgCNER, Peoples_daily, MSRA, and Resume datasets, respectively, thereby outperforming the other models. Experimental results demonstrate that GatedMan accurately identifies entities related to crop diseases and pests and exhibits high generalizability in other domains.

A survey on NLP tasks, resources and techniques for low-resource Telugu-English code-mixed text

With the proliferation of informal content on various social media platforms in the form of posts, comments, and feedback, the importance of analyzing text in code-mixed form is gaining importance. Telugu, a low-resource Indian language, has a lot of online content being generated in code-mixed form. However, the lack of large corpora, annotated data and Natural Language Processing (NLP) resources are impeding research on Telugu-English code-mixed data. This paper provides a survey of existing literature on Telugu-English code-mixed text in the areas of resources, POS tagging, Named Entity Recognition, language identification, sentiment analysis, application tasks, dialog systems, and Question-Answering. Various datasets being used by the researchers in the field, along with methods applied to them are detailed. Research gaps are identified to provide future direction for researchers working in this field.

Transformer-based approach for symptom recognition and multilingual linking.

This paper presents a transformer-based approach for symptom Named Entity Recognition (NER) in Spanish clinical texts and multilingual entity linking on the SympTEMIST dataset. For Spanish NER, we fine tune a RoBERTa-based token-level classifier with Bidirectional Long Short-Term Memory and conditional random field layers on an augmented train set, achieving an F1 score of 0.73. Entity linking is performed via a hybrid approach with dictionaries, generating candidates from a knowledge base containing Unified Medical Language System aliases using the cross-lingual SapBERT and reranking the top candidates using GPT-3.5. The entity linking approach shows consistent results for multiple languages of 0.73 accuracy on the SympTEMIST multilingual dataset and also achieves an accuracy of 0.6123 on the Spanish entity linking task surpassing the current top score for this subtask. Database URL: https://github.com/svassileva/symptemist-multilingual-linking.

MBJELEL: An End-to-End Knowledge Graph Entity Linking Method Applied to Civil Aviation Emergencies

Aviation emergency management is playing a more and more important role in the aviation field. How to make effective use of massive heterogeneous and multi-source aviation accident knowledge has become a great challenge for aviation emergency management. Aiming at the problems such as too long physical length, mixed and composite entities, similar character of domain entity names, information difference between entities, separation of codes between entities, coding errors during transmission, etc., the construction method of knowledge map of civil aviation emergencies is studied. In previous research methods, entity link is always divided into two parts, that is, first detection and then disambiguation, which makes the mentioned entity and the candidate entity are encoded separately, and there is error transmission between the two parts, modules cannot communicate with each other, and the close association between entities cannot be well learned. In this paper, we proposed an end-to-end entity linking method based on two-layer BiLSTM model joint coding vectorize each word of civil aviation text information, and then concatenate feature vectors into two-layer BiLSTM model to obtain high-level context representation. Because the joint encoding of boundary information can reduce the error transmission, information is exchanged between candidate entities during the initial encoding to enhance the closeness between candidate entities and candidate entities. The experimental results show that compared with other sota models, the F1 value of the proposed model reaches 88.97%.

Few-Shot Learning Sensitive Recognition Method Based on Prototypical Network

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853.

A Benchmark Evaluation of Multilingual Large Language Models for Arabic Cross-Lingual Named-Entity Recognition

Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large language models for Arabic cross-lingual NER. Furthermore, we investigated the performance of different MLLMs adaptation methods to better model the Arabic language. An error analysis of the different adaptation methods is presented. Our experimental results indicate that GigaBERT outperforms other models for Arabic cross-lingual NER, while language-adaptive pre-training (LAPT) proves to be the most effective adaptation method across all datasets. Our findings highlight the importance of incorporating language-specific knowledge to enhance the performance in distant language pairs like English and Arabic.

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).