Active Learning Yields Better Training Data for Scientific Named Entity Recognition

2019 ◽  
Author(s):  
Roselyne Tchoua ◽  
Aswathy Ajith ◽  
Zhi Hong ◽  
Logan Ward ◽  
Kyle Chard ◽  
...  
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity

Partial Annotation Scheme for Active Learning on Named Entity Recognition Tasks

2020 ◽  
Vol 1 (3) ◽  
pp. 319-332
Author(s):  
Koga Kobayashi ◽  
Kei Wakabayashi
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Instance Selection ◽  
Annotation Scheme ◽  
Learning Methods ◽  
Named Entity ◽  
Active Learning Method ◽  
Specific Part

Active learning is a promising approach to alleviate the expensive annotation cost for making training data on named entity recognition (NER) tasks. However, since existing active learning methods on NER tasks implicitly assume the full annotation scheme of which the unit of an annotation request is the whole sentence, the efficiency of the data instance selection is limited. In this paper, we propose a new active learning method based on a partial annotation scheme, which selects a part of the sentences to be annotated and asks human annotators to label a specific part of the target sentences. In the experiment, we show that the partial annotation scheme can quickly train the proposed point-wise prediction model compared to the existing active learning methods on NER tasks.

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 Learning Task-Specific Representation for Novel Words in Sequence Labeling

2019 ◽  
Author(s):  
Minlong Peng ◽  
Qi Zhang ◽  
Xiaoyu Xing ◽  
Tao Gui ◽  
Jinlan Fu ◽  
...  
Keyword(s):  
Empirical Studies ◽  
Named Entity Recognition ◽  
Learning Task ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Word Representation

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e.g., character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.

scholarly journals BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition

2020 ◽  
Author(s):  
Usman Naseem ◽  
Matloob Khushi ◽  
Vinay Reddy ◽  
Sakthivel Rajendran ◽  
Imran Razzak ◽  
...  
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Future Research ◽  
Named Entity ◽  
Domain Specific ◽  
Context Dependent ◽  
Biomedical Named Entity Recognition

Abstract Background: In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. Results: We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) - bioALBERT - an effective domain-specific pre-trained language model trained on huge biomedical corpus designed to capture biomedical context-dependent NER. We adopted self-supervised loss function used in ALBERT that targets on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction strategies to minimise memory usage and enhance the training time in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. The performance is increased for; (i) disease type corpora by 7.47% (NCBI-disease) and 10.63% (BC5CDR-disease); (ii) drug-chem type corpora by 4.61% (BC5CDR-Chem) and 3.89 (BC4CHEMD); (iii) gene-protein type corpora by 12.25% (BC2GM) and 6.42% (JNLPBA); and (iv) Species type corpora by 6.19% (LINNAEUS) and 23.71% (Species-800) is observed which leads to a state-of-the-art results. Conclusions: The performance of proposed model on four different biomedical entity types shows that our model is robust and generalizable in recognizing biomedical entities in text. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.

scholarly journals Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1986
Author(s):  
Liguo Yao ◽  
Haisong Huang ◽  
Kuan-Wei Wang ◽  
Shih-Huan Chen ◽  
Qiaoqiao Xiong
Keyword(s):  
Active Learning ◽  
Manufacturing Industry ◽  
Learning Strategy ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Utilization Rate ◽  
Data Types ◽  
Fine Grained ◽  
Named Entity ◽  
Model Transfer

Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.

scholarly journals Using error decay prediction to overcome practical issues of deep active learning for named entity recognition

2020 ◽  
Vol 109 (9-10) ◽  
pp. 1749-1778
Author(s):  
Haw-Shiuan Chang ◽  
Shankar Vembu ◽  
Sunil Mohan ◽  
Rheeya Uppaal ◽  
Andrew McCallum
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

scholarly journals A Low-Cost Named Entity Recognition Research Based on Active Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Han Huang ◽  
Hongyu Wang ◽  
Dawei Jin
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Selection Process ◽  
Conditional Random Field ◽  
Low Cost ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Training Set ◽  
Processing Technologies ◽  
Named Entity

Named entity recognition (NER) is an indispensable and very important part of many natural language processing technologies, such as information extraction, information retrieval, and intelligent Q & A. This paper describes the development of the AL-CRF model, which is a NER approach based on active learning (AL). The algorithmic sequence of the processes performed by the AL-CRF model is the following: first, the samples are clustered using the k-means approach. Then, stratified sampling is performed on the produced clusters in order to obtain initial samples, which are used to train the basic conditional random field (CRF) classifier. The next step includes the initiation of the selection process which uses the criterion of entropy. More specifically, samples having the highest entropy values are added to the training set. Afterwards, the learning process is repeated, and the CRF classifier is retrained based on the obtained training set. The learning and the selection process of the AL is running iteratively until the harmonic mean F stabilizes and the final NER model is obtained. Several NER experiments are performed on legislative and medical cases in order to validate the AL-CRF performance. The testing data include Chinese judicial documents and Chinese electronic medical records (EMRs). Testing indicates that our proposed algorithm has better recognition accuracy and recall rate compared to the conventional CRF model. Moreover, the main advantage of our approach is that it requires fewer manually labelled training samples, and at the same time, it is more effective. This can result in a more cost effective and more reliable process.

Sign in / Sign up

Export Citation Format

Share Document