Domain based Chunking

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.

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

10.21203/rs.3.rs-125685/v1 ◽

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.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04200-w ◽

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.

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

Processes ◽

10.3390/pr9071178 ◽

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.

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

Information ◽

10.3390/info11020079 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79 ◽

Cited By ~ 2

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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Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

ASM Science Journal ◽

10.32802/asmscj.2021.758 ◽

2021 ◽

Vol 16 ◽

pp. 1-10

Author(s):

Husni Teja Sukmana ◽

JM Muslimin ◽

Asep Fajar Firmansyah ◽

Lee Kyung Oh

Keyword(s):

Named Entity Recognition ◽

General Purpose ◽

Entity Recognition ◽

Basic Knowledge ◽

Knowledge Graph ◽

Specific Domain ◽

Named Entity ◽

Description Framework ◽

General Source ◽

Domain Information

In Indonesia, philanthropy is identical to Zakat. Zakat belongs to a specific domain because it has its characteristics of knowledge. This research studied knowledge graph in the Zakat domain called KGZ which is conducted in Indonesia. This area is still rarely performed, thus it becomes the first knowledge graph for Zakat in Indonesia. It is designed to provide basic knowledge on Zakat and managing the Zakat in Indonesia. There are some issues with building KGZ, firstly, the existing Indonesian named entity recognition (NER) is non-restricted and general-purpose based which data is obtained from a general source like news. Second, there is no dataset for NER in the Zakat domain. We define four steps to build KGZ, involving data acquisition, extracting entities and their relationship, mapping to ontology, and deploying knowledge graphs and visualizations. This research contributed a knowledge graph for Zakat (KGZ) and a building NER model for Zakat, called KGZ-NER. We defined 17 new named entity classes related to Zakat with 272 entities, 169 relationships and provided labelled datasets for KGZ-NER that are publicly accessible. We applied the Indonesian-Open Domain Information Extractor framework to process identifying entities’ relationships. Then designed modeling of information using resources description framework (RDF) to build the knowledge base for KGZ and store it to GraphDB, a product from Ontotext. This NER model has a precision 0.7641, recall 0.4544, and F1-score 0.5655. The increasing data size of KGZ is required to discover all of the knowledge of Zakat and managing Zakat in Indonesia. Moreover, sufficient resources are required in future works.

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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9010015 ◽

2019 ◽

Vol 9 (1) ◽

pp. 15 ◽

Cited By ~ 2

Author(s):

Runyu Fan ◽

Lizhe Wang ◽

Jining Yan ◽

Weijing Song ◽

Yingqian Zhu ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Conditional Random Field ◽

Named Entity Recognition ◽

Entity Recognition ◽

Knowledge Graph ◽

Geological Hazard ◽

Geological Hazards ◽

Named Entity ◽

Corpus Construction

Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.

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Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

Applied Sciences ◽

10.3390/app9183658 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3658 ◽

Cited By ~ 6

Author(s):

Jianliang Yang ◽

Yuenan Liu ◽

Minghui Qian ◽

Chenghua Guan ◽

Xiangfei Yuan

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Large Scale ◽

Short Term Memory ◽

Conditional Random Field ◽

Named Entity Recognition ◽

Recognition Task ◽

Entity Recognition ◽

Language Models ◽

Named Entity

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

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The sale of heritage on eBay: Market trends and cultural value

Big Data & Society ◽

10.1177/2053951720968865 ◽

2020 ◽

Vol 7 (2) ◽

pp. 205395172096886

Author(s):

Mark Altaweel ◽

Tasoula Georgiou Hadjitofi

Keyword(s):

Language Processing ◽

Large Scale ◽

Conditional Random Fields ◽

Named Entity Recognition ◽

Entity Recognition ◽

Cultural Value ◽

Named Entity ◽

Online Marketplace ◽

Large Scale Analysis ◽

Market Trends

The marketisation of heritage has been a major topic of interest among heritage specialists studying how the online marketplace shapes sales. Missing from that debate is a large-scale analysis seeking to understand market trends on popular selling platforms such as eBay. Sites such as eBay can inform what heritage items are of interest to the wider public, and thus what is potentially of greater cultural value, while also demonstrating monetary value trends. To better understand the sale of heritage on eBay’s international site, this work applies named entity recognition using conditional random fields, a method within natural language processing, and word dictionaries that inform on market trends. The methods demonstrate how Western markets, particularly the US and UK, have dominated sales for different cultures. Roman, Egyptian, Viking (Norse/Dane) and Near East objects are sold the most. Surprisingly, Cyprus and Egypt, two countries with relatively strict prohibition against the sale of heritage items, make the top 10 selling countries on eBay. Objects such as jewellery, statues and figurines, and religious items sell in relatively greater numbers, while masks and vessels (e.g. vases) sell at generally higher prices. Metal, stone and terracotta are commonly sold materials. More rare materials, such as those made of ivory, papyrus or wood, have relatively higher prices. Few sellers dominate the market, where in some months 40% of sales are controlled by the top 10 sellers. The tool used for the study is freely provided, demonstrating benefits in an automated approach to understanding sale trends.

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Named Entity Recognition with Bidirectional LSTM-CNNs

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00104 ◽

2016 ◽

Vol 4 ◽

pp. 357-370 ◽

Cited By ~ 319

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.

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MasakhaNER: Named Entity Recognition for African Languages

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00416 ◽

2021 ◽

Vol 9 ◽

pp. 1116-1131

Author(s):

David Ifeoluwa Adelani ◽

Jade Abbott ◽

Graham Neubig ◽

Daniel D’souza ◽

Julia Kreutzer ◽

...

Keyword(s):

Transfer Learning ◽

State Of The Art ◽

Empirical Evaluation ◽

Named Entity Recognition ◽

Entity Recognition ◽

Future Research ◽

African Continent ◽

High Quality ◽

African Languages ◽

Named Entity

Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

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