scholarly journals Study on structured method of Chinese MRI report of nasopharyngeal carcinoma

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Xin Huang ◽  
Hui Chen ◽  
Jing-Dong Yan
Keyword(s):  
High Performance ◽  
Nasopharyngeal Cancer ◽  
Named Entity Recognition ◽  
Text Structure ◽  
Entity Recognition ◽  
Knowledge Network ◽  
Medical Field ◽  
Text Data ◽  
Named Entity ◽  
Extraction Model

Abstract Background Image text is an important text data in the medical field at it can assist clinicians in making a diagnosis. However, due to the diversity of languages, most descriptions in the image text are unstructured data. The same medical phenomenon may also be described in various ways, such that it remains challenging to conduct text structure analysis. The aim of this research is to develop a feasible approach that can automatically convert nasopharyngeal cancer reports into structured text and build a knowledge network. Methods In this work, we compare commonly used named entity recognition (NER) models, choose the optimal model as our triplet extraction model, and present a Chinese structuring algorithm. Finally, we visualize the results of the algorithm in the form of a knowledge network of nasopharyngeal cancer. Results In NER, both accuracy and recall of the BERT-CRF model reached 99%. The structured extraction rate is 84.74%, and the accuracy is 89.39%. The architecture based on recurrent neural network does not rely on medical dictionaries or word segmentation tools and can realize triplet recognition. Conclusions The BERT-CRF model has high performance in NER, and the triplet can reflect the content of the image report. This work can provide technical support for the construction of a nasopharyngeal cancer database.

scholarly journals Named Entity Recognition with Bidirectional LSTM-CNNs

2016 ◽  
Vol 4 ◽  
pp. 357-370 ◽  
Author(s):  
Jason P.C. Chiu ◽  
Eric Nichols
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Feature Engineering ◽  
Named Entity ◽  
Extensive Evaluation ◽  
Bidirectional Lstm ◽  
Art Performance

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.

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.

A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators

2012 ◽  
Vol 3 (1) ◽  
pp. 55-71 ◽  
Author(s):  
O. Isaac Osesina ◽  
John Talburt
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
Business Processes ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Substantial Part ◽  
Data Intensive ◽  
Named Entity ◽  
Unstructured Text ◽  
Wide Availability

Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.

scholarly journals Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

2021 ◽  
Vol 47 (1) ◽  
pp. 117-140
Author(s):  
Oshin Agarwal ◽  
Yinfei Yang ◽  
Byron C. Wallace ◽  
Ani Nenkova
Keyword(s):  
High Performance ◽  
Human Study ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Model Errors ◽  
Linguistic Context ◽  
Named Entity ◽  
Future Work ◽  
Current Systems ◽  
Main Factor

Abstract Named entity recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capable of interpreting the text and inferring the correct entity type from the linguistic context? We examine these questions by contrasting the performance of several variants of architectures for named entity recognition, with some provided only representations of the context as features. We experiment with GloVe-based BiLSTM-CRF as well as BERT. We find that context does influence predictions, but the main factor driving high performance is learning the named tokens themselves. Furthermore, we find that BERT is not always better at recognizing predictive contexts compared to a BiLSTM-CRF model. We enlist human annotators to evaluate the feasibility of inferring entity types from context alone and find that humans are also mostly unable to infer entity types for the majority of examples on which the context-only system made errors. However, there is room for improvement: A system should be able to recognize any named entity in a predictive context correctly and our experiments indicate that current systems may be improved by such capability. Our human study also revealed that systems and humans do not always learn the same contextual clues, and context-only systems are sometimes correct even when humans fail to recognize the entity type from the context. Finally, we find that one issue contributing to model errors is the use of “entangled” representations that encode both contextual and local token information into a single vector, which can obscure clues. Our results suggest that designing models that explicitly operate over representations of local inputs and context, respectively, may in some cases improve performance. In light of these and related findings, we highlight directions for future work.

scholarly journals New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1485
Author(s):  
Brahim Ait Benali ◽  
Soukaina Mihi ◽  
Ismail El Bazi ◽  
Nabil Laachfoubi
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Feature Selection ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Support Vector ◽  
Text Data ◽  
Impact Performance ◽  
Named Entity ◽  
Feature Selection Approach

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.

Answer Extraction Model Based on Named Entity Recognition

2014 ◽  
Vol 571-572 ◽  
pp. 339-344
Author(s):  
Yong He Lu ◽  
Ming Hui Liang
Keyword(s):  
Question Answering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Model Based ◽  
Answer Extraction ◽  
Proposed Model ◽  
Final Answer ◽  
Extraction Model

The answer extraction model has a direct impact on the performance of the Automatic Question Answering System (QA System). In this paper, an answer extraction model based on named entity recognition was presented. It mainly answers specific questions whose answers are related with the named entity. Firstly, it classified the questions according to answer types. And then it identified named entities with suitable types in the fragmented information. Finally, it got the final answer based on scores. The experiments in the paper proved that the model could accurately answer the questions provided by Text REtrieval Conference (TREC). Thus, the proposed model is easy to implement and its performance is good for specific questions.

scholarly journals Combining Minimally-supervised Methods for Arabic Named Entity Recognition

2015 ◽  
Vol 3 ◽  
pp. 243-255 ◽  
Author(s):  
Maha Althobaiti ◽  
Udo Kruschwitz ◽  
Massimo Poesio
Keyword(s):  
High Performance ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classifier Combination ◽  
Named Entity ◽  
Distant Learning ◽  
Novel Approach ◽  
Learning Techniques ◽  
Supervised Methods ◽  
Minimally Supervised

Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.

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