Named Entity Recognition Research Articles

Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models

With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10–50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the “organization” element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome.

Named Entity Recognition in Bengali and Hindi Using MuRIL and Conditional Random Fields

Named Entity Recognition in Bengali and Hindi Using MuRIL and Conditional Random Fields

A semi-supervised framework fusing multiple information for knowledge graph entity alignment

Entity alignment (EA) is a fundamental task for cross linguistic knowledge graphs (KGs) understanding and interaction, which is committed to matching entities from different graphs based on their inherent semantics. Methods based on graph neural networks (GNNs) dominate the EA task, however, the majority of them ignore the higher-order information among entities in the KGs. Meanwhile, as important auxiliary information, the relational semantics, string information of entity names and attribute information of entities are insufficiently exploited during the inference phase. In addition, labeled alignment data is universally insufficient across various datasets, which limits the performance of the model. In this paper, we propose a Semi-supervised EA framework that Comprehensively considers both Structural and Attribute information within KGs (SCSA) to address these problems above. Specifically, our approach first leverages hypergraph neural networks (HGNN) to aggregate relational semantic information and graph convolutional networks (GCNs) with a highway filtering strategy to acquire the embedding representation of entities precisely. Then, we propose a bidirectional filtering technique with a combination of entity, attribute and string values to create pseudo-labeled data and lead the model for iteratively training. We implement our proposed framework on several publicly recognized cross-lingual datasets. The experimental results indicate that our framework outperforms almost all state-of-the-art (SOTA) methods.

Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers

Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships.

Class-Imbalanced-Aware Distantly Supervised Named Entity Recognition.

Distantly supervised named entity recognition (NER), which automatically learns NER models without manually labeling data, has gained much attention recently. In distantly supervised NER, positive unlabeled (PU) learning methods have achieved notable success. However, existing PU learning-based NER methods are unable to automatically handle the class imbalance and further depend on the estimation of the unknown class prior; thus, the class imbalance and imperfect estimation of the class prior degenerate the NER performance. To address these issues, this article proposes a novel PU learning method for distantly supervised NER. The proposed method can automatically handle the class imbalance and does not need to engage in class prior estimation, which enables the proposed methods to achieve the state-of-the-art performance. Extensive experiments support our theoretical analysis and validate the superiority of our method.

Chinese EMR Named Entity Recognition Using Fused Label Relations Based on Machine Reading Comprehension Framework.

Chinese electronic medical record (EMR) presents significant challenges for named entity recognition (NER) due to their specialized nature, unique language features, and diverse expressions. Traditionally, NER is treated as a sequence labeling task, where each token is assigned a label. Recent research has reframed NER within the machine reading comprehension (MRC) framework, extracting entities in a question-answer format, achieving state-of-the-art performance. However, these MRC-based methods have a significant limitation: they extract entities of various types independently, ignoring their interrelations. To address this, we introduce the Fusion Label Relations with MRC (FLR-MRC) model, which enhances the MRC model by implicitly capturing dependencies among entity types. FLR-MRC models interrelations between labels using graph attention networks, integrating these with textual data to identify entities. On the benchmark CMeEE and CCKS2017-CNER datasets, FLR-MRC achieves F1-scores of 0.6652 and 0.9101, respectively, outperforming existing clinical NER methods.

Improving dictionary-based named entity recognition with deep learning.

Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360.

Threat intelligence named entity recognition techniques based on few-shot learning

In today’s digital and internet era, threat intelligence analysis is of paramount importance to ensure network and information security. Named Entity Recognition (NER) is a fundamental task in natural language processing, aimed at identifying and extracting specific types of named entities from text, such as person names, locations, organization names, dates, times, currencies, and more. The quality of entities determines the effectiveness of upper-layer applications such as knowledge graphs. Recently, there has been a scarcity of training data in the threat intelligence field, and single models suffer from poor generalization ability. To address this, we propose a multi-view learning model, named the Few-shot Threat Intelligence Named Entity Recognition Model (FTM). We enhance the fusion method based on FTM, and further propose the FTM-GRU (Gate Recurrent Unit) model. The FTM model is based on the Tri-training algorithm to collaboratively train three few-shot NER models, leveraging the complementary nature of different model views to enable them to capture more threat intelligence domain knowledge at the coding level.FTM-GRU improves the fusion of multiple views. FTM-GRU uses the improved GRU model structure to control the memory and forgetting of view information, and introduces a relevance calculation unit to avoid redundancy of view information while highlighting important semantic features. We label and construct a few-shot Threat Intelligence Dataset (TID), and experiments on TID as well as the publicly available National Vulnerability Database (NVD) validate the effectiveness of our model for NER in the threat intelligence domain. Experimental results demonstrate that our proposed model achieves better recognition results in the task.

Joint Entity and Relation Extraction With Set Prediction Networks.

Joint entity and relation extraction is an important task in natural language processing, which aims to extract all relational triples mentioned in a given sentence. In essence, the relational triples mentioned in a sentence are in the form of a set, which has no intrinsic order between elements and exhibits the permutation invariant feature. However, previous seq2seq-based models require sorting the set of relational triples into a sequence beforehand with some heuristic global rules, which destroys the natural set structure. In order to break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model is not burdened with predicting the order of multiple triples. To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. In contrast to autoregressive approaches that generate triples one by one in a specific order, the proposed networks are able to directly output the final set of relational triples in one shot. Furthermore, we also design a set-based loss that forces unique predictions through bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities. Various experiments on two benchmark datasets demonstrate that our proposed model significantly outperforms the current state-of-the-art (SoTA) models. Training code and trained models are now publicly available at https://github.com/DianboWork/SPN4RE.

Clinical named entity extraction for extracting information from medical data

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6%.

CarD-T: Interpreting Carcinomic Lexicon via Transformers.

The identification and classification of carcinogens is critical in cancer epidemiology, necessitating updated methodologies to manage the burgeoning biomedical literature. Current systems, like those run by the International Agency for Research on Cancer (IARC) and the National Toxicology Program (NTP), face challenges due to manual vetting and disparities in carcinogen classification spurred by the volume of emerging data. To address these issues, we introduced the Carcinogen Detection via Transformers (CarD-T) framework, a text analytics approach that combines transformer-based machine learning with probabilistic statistical analysis to efficiently nominate carcinogens from scientific texts. CarD-T uses Named Entity Recognition (NER) trained on PubMed abstracts featuring known carcinogens from IARC groups and includes a context classifier to enhance accuracy and manage computational demands. Using this method, journal publication data indexed with carcinogenicity & carcinogenesis Medical Subject Headings (MeSH) terms from the last 25 years was analyzed, identifying potential carcinogens. Training CarD-T on 60% of established carcinogens (Group 1 and 2A carcinogens, IARC designation), CarD-T correctly to identifies all of the remaining Group 1 and 2A designated carcinogens from the analyzed text. In addition, CarD-T nominates roughly 1500 more entities as potential carcinogens that have at least two publications citing evidence of carcinogenicity. Comparative assessment of CarD-T against GPT-4 model reveals a high recall (0.857 vs 0.705) and F1 score (0.875 vs 0.792), and comparable precision (0.894 vs 0.903). Additionally, CarD-T highlights 554 entities that show disputing evidence for carcinogenicity. These are further analyzed using Bayesian temporal Probabilistic Carcinogenic Denomination (PCarD) to provide probabilistic evaluations of their carcinogenic status based on evolving evidence. Our findings underscore that the CarD-T framework is not only robust and effective in identifying and nominating potential carcinogens within vast biomedical literature but also efficient on consumer GPUs. This integration of advanced NLP capabilities with vital epidemiological analysis significantly enhances the agility of public health responses to carcinogen identification, thereby setting a new benchmark for automated, scalable toxicological investigations.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

3102: NSCLC Electronic Health Records Analysis by Named Entity Recognition in Italian Clinical Reports

3102: NSCLC Electronic Health Records Analysis by Named Entity Recognition in Italian Clinical Reports

KCB-FLAT: Enhancing Chinese Named Entity Recognition with Syntactic Information and Boundary Smoothing Techniques

Named entity recognition (NER) is a fundamental task in Natural Language Processing (NLP). During the training process, NER models suffer from over-confidence, and especially for the Chinese NER task, it involves word segmentation and introduces erroneous entity boundary segmentation, exacerbating over-confidence and reducing the model’s overall performance. These issues limit further enhancement of NER models. To tackle these problems, we proposes a new model named KCB-FLAT, designed to enhance Chinese NER performance by integrating enriched semantic information with the word-Boundary Smoothing technique. Particularly, we first extract various types of syntactic data and utilize a network named Key-Value Memory Network, based on syntactic information to functionalize this, integrating it through an attention mechanism to generate syntactic feature embeddings for Chinese characters. Subsequently, we employed an encoder named Cross-Transformer to thoroughly combine syntactic and lexical information to address the entity boundary segmentation errors caused by lexical information. Finally, we introduce a Boundary Smoothing module, combined with a regularity-conscious function, to capture the internal regularity of per entity, reducing the model’s overconfidence in entity probabilities through smoothing. Experimental results demonstrate that the proposed model achieves exceptional performance on the MSRA, Resume, Weibo, and self-built ZJ datasets, as verified by the F1 score.

Cross-lingual Natural Language Processing on Limited Annotated Case/Radiology Reports in English and Japanese: Insights from the Real-MedNLP Workshop.

Textual datasets (corpora) are crucial for the application of natural language processing (NLP) models. However, corpus creation in the medical field is challenging, primarily because of privacy issues with raw clinical data such as health records. Thus, the existing clinical corpora are generally small and scarce. Medical NLP (MedNLP) methodologies perform well with limited data availability. We present the outcomes of the Real-MedNLP workshop, which was conducted using limited and parallel medical corpora. Real-MedNLP exhibits three distinct characteristics: (1) Limited Annotated Documents: The training data comprises only a small set (approximately 100) of case reports (CRs) and radiology reports (RRs) that have been annotated. (2) Bilingually Parallel: The constructed corpora are parallel in Japanese and English. (3) Practical Tasks: The workshop addresses fundamental tasks, such as named entity recognition and applied practical tasks. We propose three tasks: named entity recognition (NER) of approximately 100 available documents (Task 1), NER based only on annotation guidelines for humans (Task 2), and clinical applications (Task 3) consisting of adverse drug effects (ADE) detection for CRs and identical case identification (CI) for RRs. Nine teams participated in this study. The best systems achieved 0.65 and 0.89 F1-scores for CRs and RRs in Task 1, whereas the top scores in Task 2 decreased by 50-70%. In Task 3, ADE reports were detected by up to 0.64 F1-score, and CI scored up to 0.96 binary accuracy. Most systems adopt medical-domain-specific pre-trained language models using data augmentation methods. Despite the challenge of limited corpus size in Tasks 1 and 2, recent approaches are promising because the partial match scores reached approximately 0.8-0.9 F1-scores. Task 3 applications revealed that the different availabilities of external language resources affected the performance per language.

Automated information extraction for behavioural interventions: evaluation and reflections on interdisciplinary AI development

Background The HBCP knowledge system identifies and extracts entities from randomised controlled trials of behaviour change interventions organised by a behaviour change intervention ontology (BCIO) to populate: 1) an outcome prediction tool; and 2) a research browser tool. This knowledge system requires automated information extraction algorithms to query and interpret evidence from behaviour change intervention (BCI) reports. This paper reports the results of an evaluation of the automated information extraction and reflects on the results in relation to the challenges of interdisciplinary working and collaboration. Methods The evaluation used a dataset of 117 previously unseen BCI reports to assess its performance. The automatically extracted information was compared to the full text PDF by trained annotators on essential BCIO entities required for the outcome prediction tool and research browser tool and whether the extracted information was assigned to the correct arm of the randomised trial. Essential entities were the outcome value, a selection of the most common Behaviour Change Techniques (BCTs), the mode of intervention delivery, and key population characteristics. Results The evaluation found an outcome value present in 53.85% (n=63) of the output from the information extraction system but it never extracted both the correct outcome values (interventions and control arms) and assigned them to the correct study arms (intervention and control). Although 84.62% (n=99) of the papers contained information relevant to Behaviour Change Techniques (BCTs), the information extraction algorithm correctly extracted only 58.59% (n=58) of BCTs. Conclusions The evaluation found that the information extraction algorithm did not extract the outcome values and key BCIO entities correctly against the correct arms in any of the papers in our sample, making it unsuitable for deployment in the outcome prediction and research browser tools. Several challenges with working in interdisciplinary teams were identified and discussed along with lessons learned for future work.

Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.

Biomedical relation extraction from scientific publications is a key task in biomedical natural language processing (NLP) and can facilitate the creation of large knowledge bases, enable more efficient knowledge discovery, and accelerate evidence synthesis. In this paper, building upon our previous effort in the BioCreative VIII BioRED Track, we propose an enhanced end-to-end pipeline approach for biomedical relation extraction (RE) and novelty detection (ND) that effectively leverages existing datasets and integrates state-of-the-art deep learning methods. Our pipeline consists of four tasks performed sequentially: named entity recognition (NER), entity linking (EL), RE, and ND. We trained models using the BioRED benchmark corpus that was the basis of the shared task. We explored several methods for each task and combinations thereof: for NER, we compared a BERT-based sequence labeling model that uses the BIO scheme with a span classification model. For EL, we trained a convolutional neural network model for diseases and chemicals and used an existing tool, PubTator 3.0, for mapping other entity types. For RE and ND, we adapted the BERT-based, sentence-bound PURE model to bidirectional and document-level extraction. We also performed extensive hyperparameter tuning to improve model performance. We obtained our best performance using BERT-based models for NER, RE, and ND, and the hybrid approach for EL. Our enhanced and optimized pipeline showed substantial improvement compared to our shared task submission, NER: 93.53 (+3.09), EL: 83.87 (+9.73), RE: 46.18 (+15.67), and ND: 38.86 (+14.9). While the performances of the NER and EL models are reasonably high, RE and ND tasks remain challenging at the document level. Further enhancements to the dataset could enable more accurate and useful models for practical use. We provide our models and code at https://github.com/janinaj/e2eBioMedRE/. Database URL: https://github.com/janinaj/e2eBioMedRE/.

Evaluating Named Entity Recognition: A comparative analysis of mono- and multilingual transformer models on a novel Brazilian corporate earnings call transcripts dataset

Since 2018, when the Transformer architecture was introduced, Natural Language Processing has gained significant momentum with pre-trained Transformer-based models that can be fine-tuned for various tasks. Most models are pre-trained on large English corpora, making them less applicable to other languages, such as Brazilian Portuguese. In our research, we identified two models pre-trained in Brazilian Portuguese (BERTimbau and PTT5) and two multilingual models (mBERT and mT5). BERTimbau and mBERT use only the Encoder module, while PTT5 and mT5 use both the Encoder and Decoder. Our study aimed to evaluate their performance on a financial Named Entity Recognition (NER) task and determine the computational requirements for fine-tuning and inference. To this end, we developed the Brazilian Financial NER (BraFiNER) dataset, comprising sentences from Brazilian banks’ earnings calls transcripts annotated using a weakly supervised approach. Additionally, we introduced a novel approach that reframes the token classification task as a text generation problem. After fine-tuning the models, we evaluated them using performance and error metrics. Our findings reveal that BERT-based models consistently outperform T5-based models. While the multilingual models exhibit comparable macro F1-scores, BERTimbau demonstrates superior performance over PTT5. In terms of error metrics, BERTimbau outperforms the other models. We also observed that PTT5 and mT5 generated sentences with changes in monetary and percentage values, highlighting the importance of accuracy and consistency in the financial domain. Our findings provide insights into the differing performance of BERT- and T5-based models for the NER task.

Recognition and normalization of multilingual symptom entities using in-domain-adapted BERT models and classification layers.

Due to the scarcity of available annotations in the biomedical domain, clinical natural language processing poses a substantial challenge, especially when applied to low-resource languages. This paper presents our contributions for the detection and normalization of clinical entities corresponding to symptoms, signs, and findings present in multilingual clinical texts. For this purpose, the three subtasks proposed in the SympTEMIST shared task of the Biocreative VIII conference have been addressed. For Subtask 1-named entity recognition in a Spanish corpus-an approach focused on BERT-based model assemblies pretrained on a proprietary oncology corpus was followed. Subtasks 2 and 3 of SympTEMIST address named entity linking (NEL) in Spanish and multilingual corpora, respectively. Our approach to these subtasks followed a classification strategy that starts from a bi-encoder trained by contrastive learning, for which several SapBERT-like models are explored. To apply this NEL approach to different languages, we have trained these models by leveraging the knowledge base of domain-specific medical concepts in Spanish supplied by the organizers, which we have translated into the other languages of interest by using machine translation tools. The results obtained in the three subtasks establish a new state of the art. Thus, for Subtask 1 we obtain precision results of 0.804, F1-score of 0.748, and recall of 0.699. For Subtask 2, we obtain performance gains of up to 5.5% in top-1 accuracy when the trained bi-encoder is followed by a WNT-softmax classification layer that is initialized with the mean of the embeddings of a subset of SNOMED-CT terms. For Subtask 3, the differences are even more pronounced, and our multilingual bi-encoder outperforms the other models analyzed in all languages except Swedish when combined with a WNT-softmax classification layer. Thus, the improvements in top-1 accuracy over the best bi-encoder model alone are 13% for Portuguese and 13.26% for Swedish. Database URL: https://doi.org/10.1093/database/baae087.

Characterization of Nanoparticles in Drinking Water Using Field-Flow Fractionation Coupled with Multi-Angle Light Scattering and Inductively Coupled Plasma Mass Spectrometry

The current absence of well-established and standardized methods for characterizing submicrometer- and nano-sized particles in water samples presents a significant analytical challenge. With the increasing utilization of nanomaterials, the potential for unintended exposure escalates. The widespread and persistent pollution of water by micro- and nanoplastics globally is a concern that demands attention, not only to reduce pollution but also to develop methods for analyzing these pollutants. Additionally, the analysis of naturally occurring nano entities such as bubbles and colloidal matter poses challenges due to the lack of systematic and consistent methodologies. This study presents Asymmetric Flow Field-Flow Fractionation (AF4) separation coupled with a UV-VIS spectrometer followed by Multi-Angle Light Scattering (MALS) for detection and size characterization of nanometric entities. It is coupled with an Inductively Coupled Plasma Mass Spectrometer (ICP-MS) for elemental analysis. Water samples from different sources, such as untreated mountain spring water, groundwater, and bottled drinking water, were analyzed. The system was calibrated using pure particle standards of different metallic compositions. Our study demonstrates the capability of AF4-UV-MALS-ICP-MS to detect metals such as Al, Ba, Cu, and Zn in particles of around 200 nm diameter and Mg associated with very small particles between 1.5 and 10 nm in different drinking water samples.