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Investigation of unsupervised pattern learning techniques for bootstrap construction of a medical treatment lexicon

Rong Xu, Alex Morgan, Amar K. Das, Alan Garber
2009 Proceedings of the Workshop on BioNLP - BioNLP '09   unpublished
Focusing on medical treatment concepts (e.g. drugs, medical procedures and medical devices), we have developed an unsupervised, iterative pattern learning approach for constructing a comprehensive dictionary  ...  We have investigated different methods of seeding, either with a seed pattern or seed instances (terms), and have compared different ranking methods for ranking extracted context patterns and instances  ...  Acknowledgments RX is supported by NLM training grant LM007033 and Stanford Medical School.  ... 
doi:10.3115/1572364.1572373 fatcat:tpv3cxpgujgmhkvuvmqjfdcctm

Machine learning in medicine: a practical introduction to natural language processing

Conrad J. Harrison, Chris J. Sidey-Gibbons
2021 BMC Medical Research Methodology  
Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research.  ...  The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software.  ...  Acknowledgements We would like to acknowledge and thank contributors to the University of California, Irvine Machine Learning Repository who have made large datasets available for public use.  ... 
doi:10.1186/s12874-021-01347-1 fatcat:s7a2h7qdird7tfs4rdjgwpyvyu

Improved Pattern Learning for Bootstrapped Entity Extraction

Sonal Gupta, Christopher Manning
2014 Proceedings of the Eighteenth Conference on Computational Natural Language Learning  
Bootstrapped pattern learning for entity extraction usually starts with seed entities and iteratively learns patterns and entities from unlabeled text.  ...  Our system outperforms existing pattern scoring algorithms for extracting drug-andtreatment entities from four medical forums.  ...  Acknowledgments We thank Diana MacLean for labeling the test data for calculating the inter-annotator agreement.  ... 
doi:10.3115/v1/w14-1611 dblp:conf/conll/GuptaM14 fatcat:4q5yngztdjhzzeh3ct544bfycq

Data-Driven Information Extraction from Chinese Electronic Medical Records

Dong Xu, Meizhuo Zhang, Tianwan Zhao, Chen Ge, Weiguo Gao, Jia Wei, Kenny Q. Zhu, Zhaohong Deng
2015 PLoS ONE  
It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal  ...  Discussion In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886).  ...  We used an unsupervised iterative process to bootstrap the existing lexica based on learned patterns.  ... 
doi:10.1371/journal.pone.0136270 pmid:26295801 pmcid:PMC4546596 fatcat:p4pgwqmazne5jbheoghviojkrm

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  Deep Learning Deep learning refers to "a class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction and transformation  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Automatic generation of lexica for sentiment polarity shifters

Marc Schulder, Michael Wiegand, Josef Ruppenhofer
2020 Natural Language Engineering  
Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%.  ...  Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness.  ...  We would like to thank Amy Isard for her invaluable feedback during the writing and revision of this article. We would also like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.1017/s135132492000039x fatcat:jauqzbifwnfpteeykcuzpio2py

Identifying References to Datasets in Publications [chapter]

Katarina Boland, Dominique Ritze, Kai Eckert, Brigitte Mathiak
2012 Lecture Notes in Computer Science  
To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach.  ...  In this paper, we propose a pattern induction method for the detection of study references in full texts.  ...  This work is funded by the DFG as part of the InFoLiS project (SU 647/2-1). We would like to thank Benjamin Zapilko and Christian Meilicke for their great support.  ... 
doi:10.1007/978-3-642-33290-6_17 fatcat:r47kifnyivdjfeefau2lx3hfya

A Variational Approach to Unsupervised Sentiment Analysis [article]

Ziqian Zeng, Wenxuan Zhou, Xin Liu, Zizheng Lin, Yangqin Song, Michael David Kuo, Wan Hang Keith Chiu
2020 arXiv   pre-print
We can learn a sentiment classifier by optimizing the lower bound.  ...  In this paper, we propose a variational approach to unsupervised sentiment analysis.  ...  ., 2011) identified dependency paths that link opinion words and targets via a bootstrapping process. This method only needs an initial opinion lexicon to start the bootstrapping process.  ... 
arXiv:2008.09394v1 fatcat:kmxpov65sbeizen3gtsojs5djm

Self-Supervised Euphemism Detection and Identification for Content Moderation [article]

Wanzheng Zhu, Hongyu Gong, Rohan Bansal, Zachary Weinberg, Nicolas Christin, Giulia Fanti, Suma Bhat
2021 arXiv   pre-print
Our algorithm for revealing euphemistic meanings of words is the first of its kind, as far as we are aware.  ...  Compared to the existing state of the art, which uses context-free word embeddings, our algorithm for detecting euphemisms achieves 30-400% higher detection accuracies of unlabeled euphemisms in a text  ...  It lists a set of euphemism candidates using a bootstrapping algorithm for semantic lexicon induction.  ... 
arXiv:2103.16808v1 fatcat:z44viumb25bxdmdyr66gfoxipu

Without lexicons, multiword expression identification will never fly: A position statement

Agata Savary, Silvio Cordeiro, Carlos Ramisch
2019 Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)  
We define requirements for such a minimal NLP-oriented lexicon, and we propose a roadmap for the MWE community driven by these requirements.  ...  Such lexicons need not necessarily achieve a linguistically complete modelling of MWEs' behavior, but they should provide minimal morphosyntactic information to cover some potential uses, so as to complement  ...  Acknowledgments This work was funded by the French PARSEME-FR project (ANR-14-CERA-0001). 15 We are grateful to Jakub Waszczuk and Kilian Evang for their valuable feedback at an early stage of our proposal  ... 
doi:10.18653/v1/w19-5110 dblp:conf/mwe/SavaryCR19 fatcat:d5rovrbxhnapno7ncnon45noay

Relation Extraction for Open and Closed Domain Question Answering [chapter]

Gosse Bouma, Ismail Fahmi, Jori Mur
2011 Interactive Multi-modal Question-Answering  
We show that dependency patterns enriched with semantic concept labels give accurate results for relations that are relevant for a medical question answering system.  ...  In this chapter, we present two methods for learning relation instances for relations relevant in a closed and open domain (medical) question answering system.  ...  Inspired by the good results of this system in the TREC 2001 evaluation, Ravichandran and Hovy (2002) , Fleischman et al (2003) and others investigated techniques for learning such extraction patterns  ... 
doi:10.1007/978-3-642-17525-1_8 dblp:series/tanlp/BoumaFM11 fatcat:jkoly4jsljaxfgfky3xs547j3i

A Review of Knowledge Graph-based Question and Answer System Research and Its Application in Chronic Disease Diagnosis

2021 Academic Journal of Computing & Information Science  
The question and answer system can be divided into three parts: question classification, entity recognition, and relationship extraction, for each of which a large number of techniques have been studied  ...  Finally, a question and answer system based on the knowledge graph of chronic diseases is designed to provide a proven solution for this field, in view of the problem that there are many patients with  ...  Semi-supervised learning is based on a small number of artificially added entity pair seeds and a large number of unlabeled samples, using pattern learning methods for iterative training to obtain a classification  ... 
doi:10.25236/ajcis.2021.040401 fatcat:x4nnh274wjbxpgxhfbhzx77cny

Semantic annotation for knowledge management: Requirements and a survey of the state of the art

Victoria Uren, Philipp Cimiano, José Iria, Siegfried Handschuh, Maria Vargas-Vera, Enrico Motta, Fabio Ciravegna
2006 Journal of Web Semantics  
While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents.  ...  In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems.  ...  Acknowledgements This work was funded by the Advanced Knowledge Technologies (AKT) Interdisciplinary Research Collaboration (IRC), the Designing Adaptive Information Extraction for Knowledge Management  ... 
doi:10.1016/j.websem.2005.10.002 fatcat:r2bm35nnifdcbibp2phnvyuo4e

Semantic Annotation for Knowledge Management: Requirements and a Survey of the State of the Art

Victoria Uren, Philipp Cimiano, Jose Iria, Siegfried Handschuh, Maria Vargas-Vera, Enrico Motta, Fabio Ciravegna
2006 Social Science Research Network  
While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents.  ...  In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems.  ...  Acknowledgements This work was funded by the Advanced Knowledge Technologies (AKT) Interdisciplinary Research Collaboration (IRC), the Designing Adaptive Information Extraction for Knowledge Management  ... 
doi:10.2139/ssrn.3199324 fatcat:s4kabxi2tzhr5hywjoruu3jpt4

Message from the general chair

Benjamin C. Lee
2015 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)  
Joint Learning for Coreference Resolution with Markov Logic Resolving "This-issue" Anaphora Varada Kolhatkar and Graeme Hirst Saturday 12:00pm-12:30pm -202 A (ICC) We annotate and resolve a particular  ...  To maximize the utility of the injected knowledge, we deploy a learning-based multi-sieve approach and develop novel entity-based features.  ...  Glossaries for several domains are iteratively acquired from the Web by means of a bootstrapping technique.  ... 
doi:10.1109/ispass.2015.7095776 dblp:conf/ispass/Lee15 fatcat:ehbed6nl6barfgs6pzwcvwxria
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