scholarly journals Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Huiwei Zhou ◽  
Zhe Liu ◽  
Chengkun Lang ◽  
Yibin Xu ◽  
Yingyu Lin ◽  
...  
Keyword(s):  
Large Scale ◽  
Named Entity Recognition ◽  
Knowledge Bases ◽  
Entity Recognition ◽  
High Quality ◽  
Recognition Model ◽  
Named Entity ◽  
Comparison Results ◽  
Knowledge Distillation ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. Methods To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. Results Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. Conclusions We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.

scholarly journals Improving Biomedical Named Entity Recognition with Label Re-correction and Knowledge Distillation

2020 ◽  
Author(s):  
Huiwei Zhou ◽  
Zhe Liu ◽  
Chengkun Lang ◽  
Yingyu Lin ◽  
Junjie Hou
Keyword(s):  
High Performance ◽  
Named Entity Recognition ◽  
Knowledge Bases ◽  
Entity Recognition ◽  
High Quality ◽  
Named Entity ◽  
Iterative Correction ◽  
Comparison Results ◽  
Knowledge Distillation ◽  
Biomedical Named Entity Recognition

Abstract Background: Biomedical named entities recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotation datasets, especially the limited knowledge contained in them. Methods: To remedy the above issue, we propose a novel Chemical and Disease Named Entity Recognition (CDNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance entity recognition model. Our framework is inspired by two points: 1) named entity recognition should be considered from the perspective of both coverage and accuracy; 2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two CDNER models, respectively. Finally, we compress the knowledge in the two models into a single model with knowledge distillation. Results: Experiments on the BioCreative V chemical-disease relation corpus show that knowledge from large datasets significantly improves CDNER performance, leading to new state-of-the-art results.Conclusions: We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for biomedical named entity recognition.

scholarly journals Multi-task learning for Chinese clinical named entity recognition with external knowledge

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming Cheng ◽  
Shufeng Xiong ◽  
Fei Li ◽  
Pan Liang ◽  
Jianbo Gao
Keyword(s):  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Data Driven ◽  
Named Entity ◽  
Proposed Model ◽  
Comparison Results ◽  
Benchmark Datasets ◽  
Public Datasets ◽  
F Measure

Abstract Background Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. Methods To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. Results In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. Conclusions The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

scholarly journals Domain based Chunking

2021 ◽  
Vol 10 (04) ◽  
pp. 1-14
Author(s):  
Nilamadhaba Mohapatra ◽  
Namrata Sarraf ◽  
Swapna sarit Sahu
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
High Quality ◽  
Specific Domain ◽  
Cold Starting ◽  
Named Entity ◽  
Machine Learning Model ◽  
Grammar Rules

Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.

scholarly journals Improving Biomedical Named Entity Recognition with Syntactic Information

2020 ◽  
Author(s):  
YUANHE TIAN ◽  
Wang Shen ◽  
Yan Song ◽  
Fei Xia ◽  
Min He ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Syntactic Information ◽  
Biomedical Texts ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts. The task can be challenging due to the lack of large-scale labeled training data and domain knowledge. Previous studies have shown that syntactic information can be useful for named entity recognition; however, most of them fail to weigh that information with respect to its contribution as they treat the syntactic information as gold reference. Results In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks to incorporate syntactic information, which is extracted from syntactic structures automatically generated by existing toolkits. Our approach outperforms baselines without memories and achieves new state-of-the-art results on on four biomedical datasets compared with previous studies, i.e., 85.67% on BC2GM, 94.22% on BC5CDR-chemical, 90.11% on NCBI-diease, and 76.33% on Species-800. Conclusion Experimental results on four benchmark datasets demonstrate the effectiveness of our method, where the state-of-the-art performance is achieved on all of them.

scholarly journals Linking Entities from Text to Hundreds of RDF Datasets for Enabling Large Scale Entity Enrichment

Knowledge ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Michalis Mountantonakis ◽  
Yannis Tzitzikas
Keyword(s):  
Large Scale ◽  
Named Entity Recognition ◽  
Knowledge Bases ◽  
Entity Recognition ◽  
High Increase ◽  
Online Application ◽  
Named Entity ◽  
Multiple Datasets ◽  
Rest Api ◽  
The Given

There is a high increase in approaches that receive as input a text and perform named entity recognition (or extraction) for linking the recognized entities of the given text to RDF Knowledge Bases (or datasets). In this way, it is feasible to retrieve more information for these entities, which can be of primary importance for several tasks, e.g., for facilitating manual annotation, hyperlink creation, content enrichment, for improving data veracity and others. However, current approaches link the extracted entities to one or few knowledge bases, therefore, it is not feasible to retrieve the URIs and facts of each recognized entity from multiple datasets and to discover the most relevant datasets for one or more extracted entities. For enabling this functionality, we introduce a research prototype, called LODsyndesisIE, which exploits three widely used Named Entity Recognition and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for recognizing the entities of a given text. Afterwards, it links these entities to the LODsyndesis knowledge base, which offers data enrichment and discovery services for millions of entities over hundreds of RDF datasets. We introduce all the steps of LODsyndesisIE, and we provide information on how to exploit its services through its online application and its REST API. Concerning the evaluation, we use three evaluation collections of texts: (i) for comparing the effectiveness of combining different Named Entity Recognition tools, (ii) for measuring the gain in terms of enrichment by linking the extracted entities to LODsyndesis instead of using a single or a few RDF datasets and (iii) for evaluating the efficiency of LODsyndesisIE.

scholarly journals Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1178
Author(s):  
Zhenhua Wang ◽  
Beike Zhang ◽  
Dong Gao
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Chemical System ◽  
Chemical Safety ◽  
High Quality ◽  
Data Set ◽  
Final Model ◽  
Named Entity ◽  
Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

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