• Title, Summary, Keyword: simple recurrent unit

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S2-Net: Korean Machine Reading Comprehension with SRU-based Self-matching Network (S2-Net: SRU 기반 Self-matching Network를 이용한 한국어 기계 독해)

  • Park, Cheoneum;Lee, Changki;Hong, Sulyn;Hwang, Yigyu;Yoo, Taejoon;Kim, Hyunki
    • 한국어정보학회:학술대회논문집
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    • pp.35-40
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    • 2017
  • 기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

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S2-Net: Korean Machine Reading Comprehension with SRU-based Self-matching Network (S2-Net: SRU 기반 Self-matching Network를 이용한 한국어 기계 독해)

  • Park, Cheoneum;Lee, Changki;Hong, Sulyn;Hwang, Yigyu;Yoo, Taejoon;Kim, Hyunki
    • Annual Conference on Human and Language Technology
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    • pp.35-40
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    • 2017
  • 기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

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S2-Net: Machine reading comprehension with SRU-based self-matching networks

  • Park, Cheoneum;Lee, Changki;Hong, Lynn;Hwang, Yigyu;Yoo, Taejoon;Jang, Jaeyong;Hong, Yunki;Bae, Kyung-Hoon;Kim, Hyun-Ki
    • ETRI Journal
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    • v.41 no.3
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    • pp.371-382
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    • 2019
  • Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short-term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self-matching network, used in R-Net, can have a similar effect to coreference resolution because the self-matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an $S^2-Net$ model that adds a self-matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed $S^2-Net$ model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset.

Long-term Outcomes of a Loop Electrosurgical Excision Procedure for Cervical Intraepithelial Neoplasia in a High Incidence Country

  • Sangkarat, Suthi;Ruengkhachorn, Irene;Benjapibal, Mongkol;Laiwejpithaya, Somsak;Wongthiraporn, Weerasak;Rattanachaiyanont, Manee
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.2
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    • pp.1035-1039
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    • 2014
  • Aim: To evaluate the operative, oncologic and obstetric outcomes of the loop electrosurgical excision procedure (LEEP) in cases with cervical neoplasia. Materials and Methods: A retrospective cohort study was conducted on patients who were suspected of cervical neoplasia and therefore undergoing LEEP at Siriraj Hospital, Mahidol University, Thailand, during 1995-2000. Outcome measures included operative complications in 407 LEEP patients and long-term outcomes in the 248 patients with cervical intraepithelial neoplasia (CIN) who were treated with only LEEP. Results: There were 407 patients undergoing LEEP; their mean age was $39.7{\pm}10.5$ years. The histopathology of LEEP specimens revealed that 89 patients (21.9%) had lesions ${\leq}CIN$ I, 295 patients (72.5%) had CIN II or III, and 23 patients (5.6%) had invasive lesions. Operative complications were found in 15 patients and included bleeding (n=9), and infection (n=7). After diagnostic LEEP, 133 patients underwent hysterectomy as the definite treatment for cervical neoplasia. Of 248 CIN patients who had LEEP only, seven (2.8%) had suffered recurrence after a median of 16 (range 6-93) months; one had CIN I, one had CIN II, and five had CIN III. All of these recurrent patients achieved remission on surgical treatment with re-LEEP (n=6) or simple hysterectomy (n=1). A significant factor affecting recurrent disease was the LEEP margin involved with the lesion (p=0.05). Kaplan-Meier analysis showed 5-year and 10-year disease-free survival (DFS) estimates of 99.9%. Twelve patients became pregnant a total of 14 times, resulting in 12 term deliveries and two miscarriages - one of which was due to an incompetent cervix. Conclusions: LEEP for patients with cervical neoplasia delivers favorable surgical, oncologic and obstetric outcomes.

VS3-NET: Neural variational inference model for machine-reading comprehension

  • Park, Cheoneum;Lee, Changki;Song, Heejun
    • ETRI Journal
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    • v.41 no.6
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    • pp.771-781
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    • 2019
  • We propose the VS3-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS3-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS3-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

Anomaly Detection for User Action with Generative Adversarial Networks (적대적 생성 모델을 활용한 사용자 행위 이상 탐지 방법)

  • Choi, Nam woong;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.43-62
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    • 2019
  • At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.