Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037718
Q. Liu, Shui Liu, Lemao Liu, Bo Xiao
This paper introduces a new entity linking task from a well-known online video application in industry, where both entities and mentions are represented by multiple sources but some of them may be missing. To address the issue of incomplete sources, it proposes a novel neural approach to model the linking relationship between a pair of an entity and a mention. To verify the proposed approach to this task, it further creates a large scale dataset including 70k examples. Experiments on this dataset empirically demonstrate that the proposed approach is effective over a baseline and particularly it is robust to the missing sources in some extent.
{"title":"Multiple-source Entity Linking with Incomplete Sources","authors":"Q. Liu, Shui Liu, Lemao Liu, Bo Xiao","doi":"10.1109/IALP48816.2019.9037718","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037718","url":null,"abstract":"This paper introduces a new entity linking task from a well-known online video application in industry, where both entities and mentions are represented by multiple sources but some of them may be missing. To address the issue of incomplete sources, it proposes a novel neural approach to model the linking relationship between a pair of an entity and a mention. To verify the proposed approach to this task, it further creates a large scale dataset including 70k examples. Experiments on this dataset empirically demonstrate that the proposed approach is effective over a baseline and particularly it is robust to the missing sources in some extent.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133857776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037671
Heng Zhang, Liangyu Chen
It is well known that using only one neural network can not get a satisfied accuracy for the problem of Duplicate Question Detection. In order to break through this dilemma, different neural networks are ensembled serially to strive for better accuracy. However, many problems, such as vanishing gradient or exploding gradient, will be encountered if the depth of neural network is blindly increased. Worse, the serial integration may be poor in computational performance since it is less parallelizable and needs more time to train. To solve these problems, we use ensemble learning with treating different neural networks as individual learners, calculating in parallel, and proposing a new voting mechanism to get better detection accuracy. In addition to the classical models based on recurrent or convolutional neural network, Multi-Head Attention is also integrated to reduce the correlation and the performance gap between different models. The experimental results in Quora question pairs dataset show that the accuracy of our method can reach 89.3%.
{"title":"Duplicate Question Detection based on Neural Networks and Multi-head Attention","authors":"Heng Zhang, Liangyu Chen","doi":"10.1109/IALP48816.2019.9037671","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037671","url":null,"abstract":"It is well known that using only one neural network can not get a satisfied accuracy for the problem of Duplicate Question Detection. In order to break through this dilemma, different neural networks are ensembled serially to strive for better accuracy. However, many problems, such as vanishing gradient or exploding gradient, will be encountered if the depth of neural network is blindly increased. Worse, the serial integration may be poor in computational performance since it is less parallelizable and needs more time to train. To solve these problems, we use ensemble learning with treating different neural networks as individual learners, calculating in parallel, and proposing a new voting mechanism to get better detection accuracy. In addition to the classical models based on recurrent or convolutional neural network, Multi-Head Attention is also integrated to reduce the correlation and the performance gap between different models. The experimental results in Quora question pairs dataset show that the accuracy of our method can reach 89.3%.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ialp48816.2019.9037701
{"title":"[IALP 2019 Front Matter]","authors":"","doi":"10.1109/ialp48816.2019.9037701","DOIUrl":"https://doi.org/10.1109/ialp48816.2019.9037701","url":null,"abstract":"","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132113086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037707
Yichao Cao, Miao Li, Tao Feng, Rujing Wang, Yue Wu
Question classification is a basic work in natural language processing, which has an important influence on question answering. Due to question sentences are complicated in many specific domains contain a large number of exclusive vocabulary, question classification becomes more difficult in these fields. To address the specific challenge, in this paper, we propose a novel hierarchical hybrid deep network for question classification. Specifically, we first take advantages of word2vec and a synonym dictionary to learn the distributed representations of words. Then, we exploit bi-directional long short-term memory networks to obtain the latent semantic representations of question sentences. Finally, we utilize convolutional neural networks to extract question sentence features and obtain the classification results by a fully-connected network. Besides, at the beginning of the model, we leverage the self-attention layer to capture more useful features between words, such as potential relationships, etc. Experimental results show that our model outperforms common classifiers such as SVM and CNN. Our approach achieves up to 9.37% average accuracy improvements over baseline method across our agricultural dataset.
{"title":"Improving Question Classification with Hybrid Networks","authors":"Yichao Cao, Miao Li, Tao Feng, Rujing Wang, Yue Wu","doi":"10.1109/IALP48816.2019.9037707","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037707","url":null,"abstract":"Question classification is a basic work in natural language processing, which has an important influence on question answering. Due to question sentences are complicated in many specific domains contain a large number of exclusive vocabulary, question classification becomes more difficult in these fields. To address the specific challenge, in this paper, we propose a novel hierarchical hybrid deep network for question classification. Specifically, we first take advantages of word2vec and a synonym dictionary to learn the distributed representations of words. Then, we exploit bi-directional long short-term memory networks to obtain the latent semantic representations of question sentences. Finally, we utilize convolutional neural networks to extract question sentence features and obtain the classification results by a fully-connected network. Besides, at the beginning of the model, we leverage the self-attention layer to capture more useful features between words, such as potential relationships, etc. Experimental results show that our model outperforms common classifiers such as SVM and CNN. Our approach achieves up to 9.37% average accuracy improvements over baseline method across our agricultural dataset.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132407671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037698
Yang Wei, Fu Xinyu
Native English speakers need more time to recognize capital letters in reading, yet the influence of capitals upon Chinese learners’ reading performance is seldom studied. We conducted an eye tracker experiment to explore the cognitive features of Chinese learners in reading texts containing capital letters. Effect of English proficiency on capital letter reading is also studied. The results showed that capitals significantly increase the cognitive load in Chinese learners’ reading process, complicate their cognitive processing, and lower their reading efficiency. The perception of capital letters of Chinese learners is found to be an isolated event and may influence the word superiority effect. English majors, who possess relatively stronger English logical thinking capability than non-English majors, face the same difficulty as the non-English majors do if no practice of capital letter reading have been done.
{"title":"Effects of English Capitals On Reading Performance of Chinese Learners: Evidence from Eye Tracking","authors":"Yang Wei, Fu Xinyu","doi":"10.1109/IALP48816.2019.9037698","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037698","url":null,"abstract":"Native English speakers need more time to recognize capital letters in reading, yet the influence of capitals upon Chinese learners’ reading performance is seldom studied. We conducted an eye tracker experiment to explore the cognitive features of Chinese learners in reading texts containing capital letters. Effect of English proficiency on capital letter reading is also studied. The results showed that capitals significantly increase the cognitive load in Chinese learners’ reading process, complicate their cognitive processing, and lower their reading efficiency. The perception of capital letters of Chinese learners is found to be an isolated event and may influence the word superiority effect. English majors, who possess relatively stronger English logical thinking capability than non-English majors, face the same difficulty as the non-English majors do if no practice of capital letter reading have been done.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131767970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037651
H. S. Priyadarshani, M. Rajapaksha, M. M. S. P. Ranasinghe, Kengatharaiyer Sarveswaran, G. Dias
In this paper, we focus on building models for transliteration of personal names between the primary languages of Sri Lanka-namely Sinhala, Tamil and English. Currently, a Rule-based system has been used to transliterate names between Sinhala and Tamil. However, we found that it fails in several cases. Further, there were no systems available to transliterate names to English. In this paper, we present a hybrid approach where we use machine learning and statistical machine translation to do the transliteration. We built a parallel trilingual corpus of personal names. Then we trained a machine learner to classify names based on the ethnicity as we found it is an influencing factor in transliteration. Then we took the transliteration as a translation problem and applied statistical machine translation to generate the most probable transliteration for personal names. The system shows very promising results compared with the existing rule-based system. It gives a BLEU score of 89 in all the test cases and produces the top BLEU score of 93.7 for Sinhala to English transliteration.
{"title":"Statistical Machine Learning for Transliteration: Transliterating names between Sinhala, Tamil and English","authors":"H. S. Priyadarshani, M. Rajapaksha, M. M. S. P. Ranasinghe, Kengatharaiyer Sarveswaran, G. Dias","doi":"10.1109/IALP48816.2019.9037651","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037651","url":null,"abstract":"In this paper, we focus on building models for transliteration of personal names between the primary languages of Sri Lanka-namely Sinhala, Tamil and English. Currently, a Rule-based system has been used to transliterate names between Sinhala and Tamil. However, we found that it fails in several cases. Further, there were no systems available to transliterate names to English. In this paper, we present a hybrid approach where we use machine learning and statistical machine translation to do the transliteration. We built a parallel trilingual corpus of personal names. Then we trained a machine learner to classify names based on the ethnicity as we found it is an influencing factor in transliteration. Then we took the transliteration as a translation problem and applied statistical machine translation to generate the most probable transliteration for personal names. The system shows very promising results compared with the existing rule-based system. It gives a BLEU score of 89 in all the test cases and produces the top BLEU score of 93.7 for Sinhala to English transliteration.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"521 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a novel method based on the language representation model called BERT (Bidirectional Encoder Representations from Transformers) for Obstetric assistant diagnosis on Chinese obstetric EMRs (Electronic Medical Records). To aggregate more information for final output, an enhanced layer is augmented to the BERT model. In particular, the enhanced layer in this paper is constructed based on strategy 1(A strategy) and/or strategy 2(A-AP strategy). The proposed method is evaluated on two datasets including Chinese Obstetric EMRs dataset and Arxiv Academic Paper Dataset (AAPD). The experimental results show that the proposed method based on BERT improves the F1 value by 19.58% and 2.71% over the state-of-the-art methods, and the proposed method based on BERT and the enhanced layer by strategy 2 improves the F1 value by 0.7% and 0.3% (strategy 1 improves the F1 value by 0.68% and 0.1%) over the method without adding enhanced layer respectively on Obstetric EMRs dataset and AAPD dataset.
本文提出了一种基于语言表示模型BERT (Bidirectional Encoder Representations from Transformers)的中国产科电子病历辅助诊断方法。为了为最终输出聚合更多信息，BERT模型中增加了一个增强层。特别地，本文的增强层是基于策略1(A策略)和/或策略2(A- ap策略)构建的。在中国产科EMRs数据集和Arxiv学术论文数据集(AAPD)上对该方法进行了评估。实验结果表明，基于BERT的方法在产科EMRs数据集和AAPD数据集上的F1值分别比现有方法提高了19.58%和2.71%，基于BERT和策略2的增强层的方法在产科EMRs数据集和AAPD数据集上的F1值分别比不添加增强层的方法提高了0.7%和0.3%(策略1的F1值分别提高了0.68%和0.1%)。
{"title":"BERT with Enhanced Layer for Assistant Diagnosis Based on Chinese Obstetric EMRs","authors":"Kunli Zhang, Chuang Liu, Xuemin Duan, Lijuan Zhou, Yueshu Zhao, Hongying Zan","doi":"10.1109/IALP48816.2019.9037721","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037721","url":null,"abstract":"This paper proposes a novel method based on the language representation model called BERT (Bidirectional Encoder Representations from Transformers) for Obstetric assistant diagnosis on Chinese obstetric EMRs (Electronic Medical Records). To aggregate more information for final output, an enhanced layer is augmented to the BERT model. In particular, the enhanced layer in this paper is constructed based on strategy 1(A strategy) and/or strategy 2(A-AP strategy). The proposed method is evaluated on two datasets including Chinese Obstetric EMRs dataset and Arxiv Academic Paper Dataset (AAPD). The experimental results show that the proposed method based on BERT improves the F1 value by 19.58% and 2.71% over the state-of-the-art methods, and the proposed method based on BERT and the enhanced layer by strategy 2 improves the F1 value by 0.7% and 0.3% (strategy 1 improves the F1 value by 0.68% and 0.1%) over the method without adding enhanced layer respectively on Obstetric EMRs dataset and AAPD dataset.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128578304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037691
Yong Cuo, Xiaodon Shi, T. Nyima, Yidong Chen
Statistical machine translation has made great progress in recent years, and Tibetan-Chinese machine translation has many needs. A phrase-based translation model is suitable for machine translation between Tibetan and Chinese, which have similar morphological changes. This paper studies the key technologies of phrase-based Tibetan-Chinese statistical machine translation, including phrase-translation models and reordering models, and proposes a phrase-based Tibetan-Chinese statistical machine translation prototype system. The method proposed in this paper has better accuracy than Moses, the current mainstream model, in the CWMT 2013 development set, and shows great performance improvement.
{"title":"Phrase-Based Tibetan-Chinese Statistical Machine Translation","authors":"Yong Cuo, Xiaodon Shi, T. Nyima, Yidong Chen","doi":"10.1109/IALP48816.2019.9037691","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037691","url":null,"abstract":"Statistical machine translation has made great progress in recent years, and Tibetan-Chinese machine translation has many needs. A phrase-based translation model is suitable for machine translation between Tibetan and Chinese, which have similar morphological changes. This paper studies the key technologies of phrase-based Tibetan-Chinese statistical machine translation, including phrase-translation models and reordering models, and proposes a phrase-based Tibetan-Chinese statistical machine translation prototype system. The method proposed in this paper has better accuracy than Moses, the current mainstream model, in the CWMT 2013 development set, and shows great performance improvement.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132385844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037653
Xuejin Yu, W. Huangfu
This paper, with the intent of solving the issues on the dating of ancient Chinese texts, takes advantage of the Long-Short Term Memory Network (LSTM) to analyze and process the character sequence in ancient Chinese. In this model, each character is transformed into a high-dimensional vector, and then vectors and the non-linear relationships among them are read and analyzed by LSTM, which finally achieve the dating tags. Experimental results show that the LSTM has a strong ability to date the ancient texts, and the precision reaches about 95% in our experiments. Thus, the proposed model offers an effective method on how to date the ancient Chinese texts. It also inspires us to actively improve the time-consuming analysis tasks in the Chinese NLP field.
{"title":"A Machine Learning Model for the Dating of Ancient Chinese Texts","authors":"Xuejin Yu, W. Huangfu","doi":"10.1109/IALP48816.2019.9037653","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037653","url":null,"abstract":"This paper, with the intent of solving the issues on the dating of ancient Chinese texts, takes advantage of the Long-Short Term Memory Network (LSTM) to analyze and process the character sequence in ancient Chinese. In this model, each character is transformed into a high-dimensional vector, and then vectors and the non-linear relationships among them are read and analyzed by LSTM, which finally achieve the dating tags. Experimental results show that the LSTM has a strong ability to date the ancient texts, and the precision reaches about 95% in our experiments. Thus, the proposed model offers an effective method on how to date the ancient Chinese texts. It also inspires us to actively improve the time-consuming analysis tasks in the Chinese NLP field.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037719
Wenwei Dong, Yanlu Xie
Due to the difficulties of collecting and annotating second language (L2) learner’s speech corpus in Computer-Assisted Pronunciation Training (CAPT), traditional mispronunciation detection framework is similar to ASR, it uses speech corpus of native speaker to train neural networks and then the framework is used to evaluate non-native speaker’s pronunciation. Therefore there is a mismatch between them in channels, reading style, and speakers. In order to reduce this influence, this paper proposes a feature adaptation method using Correlational Neural Network (CorrNet). Before training the acoustic model, we use a few unannotated non-native data to adapt the native acoustic feature. The mispronunciation detection accuracy of CorrNet based method has improved 3.19% over un-normalized Fbank feature and 1.74% over bottleneck feature in Japanese speaking Chinese corpus. The results show the effectiveness of the method.
{"title":"Correlational Neural Network Based Feature Adaptation in L2 Mispronunciation Detection","authors":"Wenwei Dong, Yanlu Xie","doi":"10.1109/IALP48816.2019.9037719","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037719","url":null,"abstract":"Due to the difficulties of collecting and annotating second language (L2) learner’s speech corpus in Computer-Assisted Pronunciation Training (CAPT), traditional mispronunciation detection framework is similar to ASR, it uses speech corpus of native speaker to train neural networks and then the framework is used to evaluate non-native speaker’s pronunciation. Therefore there is a mismatch between them in channels, reading style, and speakers. In order to reduce this influence, this paper proposes a feature adaptation method using Correlational Neural Network (CorrNet). Before training the acoustic model, we use a few unannotated non-native data to adapt the native acoustic feature. The mispronunciation detection accuracy of CorrNet based method has improved 3.19% over un-normalized Fbank feature and 1.74% over bottleneck feature in Japanese speaking Chinese corpus. The results show the effectiveness of the method.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126014198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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