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Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

JMIR MEDICAL INFORMATICS 2020年第5期8卷 e17637页

作者： Zhang, Zhichang Zhu, Lin Yu, Peilin Univ Northwest Normal Coll Comp Sci & Engn 967 Arming East Rd Lanzhou Peoples R China

Background: Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition. Objective: This study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities. methods: In this paper, the pretraining language representation model was investigated;utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags. Results: The best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments show

关键词： medical entity recognition multi-level representation learning Chinese natural language processing electronic medical records multi-head attention mechanism

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Improving rare disease classification using imperfect knowledge graph 7

Improving rare disease classification using imperfect knowle...

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2nd International workshop on Health natural language processing (HealthNLP)

作者： Li, Xuedong Wang, Yue Wang, Dongwu Yuan, Walter Peng, Dezhong Mei, Qiaozhu Sichuan Univ Coll Comp Sci Chengdu Peoples R China Univ North Carolina Chapel Hill Sch Informat & Lib Sci Chapel Hill NC USA MobLab Inc Pasadena CA USA Univ Michigan Sch Informat Ann Arbor MI 48109 USA

ISBN: (纸本)9781538691397;9781538691380

Background: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect. Method: We develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories. Results: On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F-1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation. Conclusion: Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.

关键词： Rare disease diagnosis Knowledge graph Machine learning Text classification Extremely imbalanced data

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Fusing Document, Collection and Label graph-based Representations with Word Embeddings for Text Classification 12

Fusing Document, Collection and Label Graph-based Representa...

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12th workshop on graph-based methods for natural language processing, Textgraphs 2018 - in conjunction with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human, NAACL HLT 2018

作者： Skianis, Konstantinos Malliaros, Fragkiskos D. Vazirgiannis, Michalis École Polytechnique France CentraleSupélec and Inria Saclay France

ISBN: (纸本)9781948087254

Contrary to the traditional Bag-of-Words approach, we consider the graph-of-Words (GoW) model in which each document is represented by a graph that encodes relationships between the different terms. based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce novel graph-based weighting schemes by enriching graphs with word-embedding similarities, in order to reward or penalize semantic relationships. Our methods produce more discriminative feature weights for text categorization, outperforming existing frequency-based criteria. Code and data are available online. © 2018 Association for Computational Linguistics.

关键词： Embeddings

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Findings of the third workshop on neural generation and translation 3

Findings of the third workshop on neural generation and tran...

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3rd workshop on Neural Generation and Translation, EMNLP-IJCNLP 2019

作者： Hayashi, Hiroaki Oda, Yusuke Birch, Alexandra Konstas, Ioannis Finch, Andrew Luong, Minh-Thang Neubig, Graham Sudoh, Katsuhito Carnegie Mellon University United States Google Brain University of Edinburgh United Kingdom Heriot-Watt University United Kingdom Apple Nara Institute of Science and Technology Japan

ISBN: (纸本)9781950737833

This document describes the findings of the Third workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical methods in natural language processing (EMNLP 2019). first, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language. © 2019 Association for Computational Linguistics.

关键词： Neural machine translation

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Search for K: Assessing Five Topic-Modeling Approaches to 120,000 Canadian Articles

Search for K: Assessing Five Topic-Modeling Approaches to 12...

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IEEE International Conference on Big Data (Big Data)

作者： Fu, Qiang Zhuang, Yufan Gu, Jiaxin Zhu, Yushu Qin, Huihui Guo, Xin Univ British Columbia Dept Sociol Vancouver BC Canada Columbia Univ Data Sci Inst New York NY USA Simon Fraser Univ Urban Studies Program Vancouver BC Canada Simon Fraser Univ Sch Publ Policy Vancouver BC Canada Hong Kong Polytech Univ Dept Appl Math Hong Kong Peoples R China

ISBN: (纸本)9781728108582

Topic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic modeling is still, if not increasingly, challenged by two critical issues. first, despite intense efforts toward nonparametric/post-training methods, the search for the optimal number of topics K remains a fundamental question in topic modeling and warrants input from domain experts. Second, with the development of more sophisticated models, topic modeling is now ironically been treated as a black box and it becomes increasingly difficult to tell how research findings are informed by data, model specifications, or inference algorithms. based on about 120,000 newspaper articles retrieved from three major Canadian newspapers (Globe and Mail, Toronto Star, and National Post) since 1977, we employ five methods with different model specifications and inference algorithms (Latent Semantic Analysis, Latent Dirichlet Allocation, Principal Component Analysis, Factor Analysis, Nonnegative Matrix Factorization) to identify discussion topics. The optimal topics are then assessed using three measures: coherence statistics, held-out likelihood (loss), and graph-based dimensionality selection. Mixed findings from this research complement advances in topic modeling and provide insights into the choice of optimal topics in social science research.

关键词： Topic Modeling natural language processing Social Science Optimal Number of Topics

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基于Span方法和多叉解码树的实体关系抽取

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计算机技术与发展 2023年第5期33卷 152-158,166页

作者：张鑫冼广铭梅灏洋周岑钰刘赢方华南师范大学软件学院广东佛山528225

实体关系抽取作为自然语言处理领域的一项关键技术,在构建知识图谱、信息检索等领域有着极为重要的意义。然实体关系抽取模型普遍存在词与词之间依赖性运用不足、实体识别效果低下以及单解码带来的三元组强行执行某种不必要顺序的问题... 详细信息

实体关系抽取作为自然语言处理领域的一项关键技术,在构建知识图谱、信息检索等领域有着极为重要的意义。然实体关系抽取模型普遍存在词与词之间依赖性运用不足、实体识别效果低下以及单解码带来的三元组强行执行某种不必要顺序的问题。为了解决这三个方面的问题,提升模型的性能,提出了一种新的实体关系抽取模型。该模型首先运用提取特征能力更强的BERT预训练模型获取句子表征,然后采用图卷积神经网络来增强实体与关系之间的依赖关系,再使用对实体提取能力更强的Span方法(识别实体的神经网络方法)进行实体抽取,最后采用深度多叉解码树实施并行解码得到相应的关系三元组。在CoNLL04、ADE数据集上的实验结果表明,与其他的关系抽取基线模型相比,该模型的F1值具有较好的提升,同时也验证了该文模型的有效性与泛化能力。

关键词：实体识别关系抽取深度学习预训练模型多叉解码树图神经网络

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Mining the Biographical Dictionary of Republican China, from Print to Network Exploration 3

Mining the Biographical Dictionary of Republican China, from...

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3rd Conference on Biographical Data in a Digital World, BD 2019

作者： Magistry, Pierre Armand, Cécile Henriot, Christian Aix Marseille Univ CNRS IrAsia Marseille France

This article describes preliminary experiments conducted in the context of the ENP-China project, which examines the transformation of elites in modern China. The project is centered on exploiting information from untapped textual sources at a large scale which requires to investigate new methodologies for data-rich history and to rely on natural language processing (NLP). The first experiments presented in this paper were designed on a smaller scale and better-known materials to test and develop adequate tools and methodology - at a humanly manageable scale - before eventually enlarging the corpus and scale of analysis. We focus on the Biographical Dictionary of Republican China (BDRC) edited by H. Boorman and aim at extracting biographical information and transforming a conventional dictionary into a reservoir of data on elites in modern China. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Biography China Data Mining graph Visualization History NLP

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Automatic Perturbation Analysis on General Computational graphs

arXiv

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arXiv 2020年

作者： Xu, Kaidi Shi, Zhouxing Zhang, Huan Huang, Minlie Chang, Kai-Wei Kailkhura, Bhavya Lin, Xue Hsieh, Cho-Jui Northeastern University Tsinghua University UCLA

Linear relaxation based perturbation analysis for neural networks, which aims to compute tight linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. However, the majority of linear relaxation based methods only consider feed-forward ReLU networks. While several works extended them to relatively complicated networks, they often need tedious manual derivations and implementation which are arduous and error-prone. Their limited flexibility makes it difficult to handle more complicated tasks. In this paper, we take a significant leap by developing an automatic perturbation analysis algorithm to enable perturbation analysis on any neural network structure, and its computation can be done automatically in a similar manner as the back-propagation algorithm for gradient computation. The main idea is to express a network as a computational graph and then generalize linear relaxation algorithms such as CROWN as a graph algorithm. Our algorithm itself is differentiable and integrated with PyTorch, which allows to optimize network parameters to reshape bounds into desired specifications, enabling automatic robustness verification and certified defense. In particular, we demonstrate a few tasks that are not easily achievable without an automatic framework. We first perform certified robust training and robustness verification for complex natural language models which could be challenging with manual derivation and implementation. We further show that our algorithm can be used for tasks beyond certified defense – we create a neural network with a provably flat optimization landscape and study its generalization capability, and we show that this network can preserve accuracy better after aggressive weight quantization. Code is available at https://***/KaidiXu/auto_LiRPA. Copyright © 2020, The Authors. All rights reserved.

关键词： natural language processing systems

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Efficient Generation and processing of Word Co-occurrence Networks Using corpus2graph 12

Efficient Generation and Processing of Word Co-occurrence Ne...

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作者： Zhang, Zheng Yin, Ruiqing Zweigenbaum, Pierre LIMSI CNRS Université Paris-Saclay Orsay France LRI Univ. Paris-Sud CNRS Université Paris-Saclay Orsay France

ISBN: (纸本)9781948087254

Corpus2graph is an open-source NLP-application-oriented Python package that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed. © 2018 Association for Computational Linguistics.

关键词： Python

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Neural graph Embedding methods for natural language processing

Neural Graph Embedding methods for Natural Language Processi...

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作者： Shikhar Vashishth Indian Institute of Science

学位级别：博士

graphs are all around us, ranging from citation and social networks to Knowledge graphs (KGs). They are one of the most expressive data structures which have been used to model a variety of problems. Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs; examples include DBpedia, YAGO, NELL, and Freebase. However, all of them tend to be sparse with very few facts per entity. For instance, NELL KG consists of only 1.34 facts per entity. In the first part of the thesis, we propose three solutions to alleviate this problem: (1) KG Canonicalization, i. e., identifying and merging duplicate entities in a KG, (2) Relation Extraction which involves automating the process of extracting semantic relationships between entities from unstructured text, and (3) Link prediction which includes inferring missing facts based on the known facts in a KG. For KG Canonicalization, we propose CESI (Canonicalization using Embeddings and Side Infor- mation), a novel approach which performs canonicalization over learned embeddings of Open KGs. The method extends recent advances in KG embedding by incorporating relevant NP and relation phrase side information in a principled manner. For relation extraction, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KGs for improved relation extraction. Finally, for link prediction, we propose InteractE which extends ConvE, a convolutional neural network-based link prediction method, by increasing the number of feature interaction through three key ideas feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments on multiple datasets, we demonstrate the effectiveness of our proposed methods. Traditional Neural Networks like Convolutional Networks and Recurrent Neural Networks

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