Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval最新文献_第2页

Big Data Framework for Scalable and Efficient Biomedical Literature Mining in the Cloud 云中可扩展和高效生物医学文献挖掘的大数据框架

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342843

Zhengru Shen, Xi Wang, M. Spruit

{"title":"Big Data Framework for Scalable and Efficient Biomedical Literature Mining in the Cloud","authors":"Zhengru Shen, Xi Wang, M. Spruit","doi":"10.1145/3342827.3342843","DOIUrl":"https://doi.org/10.1145/3342827.3342843","url":null,"abstract":"The massive size of available biomedical literature requires researchers to utilize novel big data technologies in data storage and analysis. Among them is cloud computing which has become the most popular solution for big data applications in industry. However, many bioinformaticians still rely on expensive and inefficient in-house infrastructure to discover knowledge from biomedical literature. Although some cloud-based solutions were constructed recently, they failed to sufficiently address a few key issues including scalability, flexibility, and reusability. Moreover, no study has taken computational cost into consideration. To fill the gap, we proposed a cloud-based big data framework that enables researchers to perform reproducible and scalable large-scale biomedical literature mining in an efficient and cost-effective way. Additionally, a cloud agnostic platform was constructed and then evaluated on two open access corpora with millions of full-text biomedical articles. The results indicate that our framework supports scalable and efficient large-scale biomedical literature mining.","PeriodicalId":254461,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval","volume":"144 5-6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129464721","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}

引用次数: 1

Speech Error Detection depending on Linguistic Units 基于语言单位的语音错误检测

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342840

Seiya Komatsu, M. Sasayama

{"title":"Speech Error Detection depending on Linguistic Units","authors":"Seiya Komatsu, M. Sasayama","doi":"10.1145/3342827.3342840","DOIUrl":"https://doi.org/10.1145/3342827.3342840","url":null,"abstract":"In this research, we aim at the construction of a system which detects, points out and corrects speech error (slip of the tongue) of a human speech that occurs in a dialogue system (example: Pepper, Amazon Echo, Google Home) and a human dialogue. In the present dialogue system, even if human makes a speech error, the system cannot recognize it, which could lead to broken communication. So far, we have created a system to detect speech error using deep learning. In this study, we propose a method to augmented training data used for deep learning. The training data is a corpus that collects examples of speech error. At present, the number of training data is insufficient to detect with high accuracy. Therefore, it is necessary to augment the training data. Specifically, the feature of the speech error is examined from an existing speech error corpus, and extended rules are created. The data augmentation of training data is performed by generating dialogue sentence which made the speech error based on the rule. As a result of evaluation experiment, detection accuracy was improved in LSTM model by data augmentation.","PeriodicalId":254461,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116298707","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}

引用次数: 3

KELDEC

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342849

Kanika, S. Chakraverty, P. Chakraborty, Shikhar Agnihotri, S. Mohapatra, Prakriti Bansal

引用次数: 3

The Study of Learning Achievement of Learners Classified VARK Learning Style in Blended Learning 混合式学习中学习者分类VARK学习风格的学习成绩研究

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342839

Beesuda Daoruang, Krich Sintanakul, A. Mingkhwan

引用次数: 5

Applying Deep Learning in Word Embedding for Making a Diagnosis Prediction Model from Orthopedic Clinical Note 应用深度学习的词嵌入构建骨科临床记录诊断预测模型

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342848

Tanakorn Rattanajariya, K. Piromsopa

引用次数: 2

An automated new approach in fast text classification (fastText): A case study for Turkish text classification without pre-processing 一种自动化的快速文本分类新方法(fastText):土耳其文本分类无预处理的案例研究

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342828

Birol Kuyumcu, Cüneyt Aksakalli, Selman Delil

引用次数: 30

A Construction of Hybrid Structural Thai Treebank 混合结构泰国树堤的构建

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342842

Pannathorn Naksung, Chayaphat Nicrothanon, Putthichot Chunjiree, Thodsaporn Chay-intr, T. Theeramunkong

{"title":"A Construction of Hybrid Structural Thai Treebank","authors":"Pannathorn Naksung, Chayaphat Nicrothanon, Putthichot Chunjiree, Thodsaporn Chay-intr, T. Theeramunkong","doi":"10.1145/3342827.3342842","DOIUrl":"https://doi.org/10.1145/3342827.3342842","url":null,"abstract":"It is possible to include complicated structures into an individual syntactic tree, to enhance the usefulness of parsed text corpus. In this part, existing works on Thai treebank construction have been developed in order to address the lack of high-level syntactic resources. However, it has yet to be sufficient for Thai Natural Language Processing. Furthermore, Thai treebanks have either syntactic or dependency structure only. This paper presents a construction of hybrid structural Thai treebank which includes both syntactic/dependency structure, a tool for conversion between constituency and dependency parse tree, and a web-based GUI for parse tree visualization. Towards the hybrid treebank construction, hundreds of constituent tree are manually annotated with predicate header to each phrase. Once the set of annotated constituent trees are obtained, the conversion procedure will be performed by determining the annotated head and its dependents. As our experiments, features of hybrid treebank are extracted and illustrated. Finally, difficulties and issues in constructing the hybrid Thai treebank are discussed.","PeriodicalId":254461,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121995600","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}

引用次数: 0

The Evaluation of Thai Poem's Content Consistency using Siamese Network 基于暹罗网络的泰国诗歌内容一致性评价

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342855

Nuttachot Promrit, S. Waijanya, Kran Thaweesith

引用次数: 4

Zero-Shot Multilingual Sentiment Analysis using Hierarchical Attentive Network and BERT 基于层次关注网络和BERT的零概率多语言情感分析

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342850

A. Sarkar, Sujeeth Reddy, Raghu Sesha Iyengar

引用次数: 10

Using Centroid Keywords and Word Mover's Distance for Single Document Extractive Summarization 基于质心关键词和Word Mover距离的单文档提取摘要

Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval Pub Date : 2019-06-28 DOI: 10.1145/3342827.3342852

Dauken Seitkali, R. Mussabayev

引用次数: 1