Apache Solr: a Practical Approach to Enterprise Search Teaches You How to Build an Enterprise Search Engine Using Apache Solr
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Velocity Users Guide
Velocity Users Guide The Apache Velocity Developers Version 1.5 Copyright © 2006 The Apache Software Foundation Table of Contents 1. Preface .......................................................................................................................................... 1 1.1. About this Guide .................................................................................................................. 1 1.2. Acknowledgements ............................................................................................................... 1 1.3. Intended Audience ................................................................................................................ 1 1.4. Feedback ............................................................................................................................ 1 2. What is Velocity? ........................................................................................................................... 2 2.1. The Fruit Store .................................................................................................................... 2 2.2. An introduction to the Velocity Template Language ................................................................... 3 2.3. Hello Velocity World! ........................................................................................................... 4 3. Language elements .......................................................................................................................... 5 3.1. Statements and directives -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
BENTO: a Visual Platform for Building Clinical NLP Pipelines Based on Codalab
BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab Yonghao Jin, Fei Li and Hong Yu Department of Computer Science, University of Massachusetts Lowell, MA, USA Abstract CodaLab1 is an open-source web-based plat- form for collaborative computational research. Although CodaLab has gained popularity in the research community, its interface has lim- ited support for creating reusable tools that can be easily applied to new datasets and composed into pipelines. In clinical domain, natural language processing (NLP) on med- ical notes generally involves multiple steps, like tokenization, named entity recognition, etc. Since these steps require different tools Figure 1: The architecture of BENTO. The BENTO which are usually scattered in different publi- back end stores the description files of various tools cations, it is not easy for researchers to use (e.g., pre-trained NLP models), processes static con- them to process their own datasets. In this pa- tents of the application and handles compilation of the per, we present BENTO, a workflow manage- user-defined pipelines. The CodaLab back end stores ment platform with a graphic user interface the datasets (bundles) and executes computational jobs. (GUI) that is built on top of CodaLab, to fa- The two back end servers are brought behind a single cilitate the process of building clinical NLP domain name using a reverse proxy server. pipelines. BENTO comes with a number of clinical NLP tools that have been pre-trained using medical notes and expert annotations this problem, researchers have developed CodaLab and can be readily used for various clinical as an open-source platform for researchers and soft- NLP tasks. -
Mastering Apache Velocity Joseph D. Gradecki Jim Cole
a 457949 FM.qxd 6/13/03 1:45 PM Page i Mastering Apache Velocity Joseph D. Gradecki Jim Cole Wiley Publishing, Inc. a 457949 FM.qxd 6/13/03 1:45 PM Page xii a 457949 FM.qxd 6/13/03 1:45 PM Page i Mastering Apache Velocity Joseph D. Gradecki Jim Cole Wiley Publishing, Inc. a 457949 FM.qxd 6/13/03 1:45 PM Page ii Publisher: Joe Wikert Copyeditor: Elizabeth Welch Executive Editor: Robert Elliott Compositors: Gina Rexrode and Amy Hassos Editorial Manager: Kathryn Malm Managing Editor: Vincent Kunkemueller Book Producer: Ryan Publishing Group, Inc. Copyright © 2003 by Joseph D. Gradecki and Jim Cole. All rights reserved. Published by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8700. Requests to the Publisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspoint Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4447, E-mail: [email protected]. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or com- pleteness of the contents of this book and specifically disclaim any implied warranties of mer- chantability or fitness for a particular purpose. -
Download Thesis
This electronic thesis or dissertation has been downloaded from the King’s Research Portal at https://kclpure.kcl.ac.uk/portal/ Big data approaches to investigating Child Mental Health disorder outcomes Downs, Jonathan Muir Awarding institution: King's College London The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without proper acknowledgement. END USER LICENCE AGREEMENT Unless another licence is stated on the immediately following page this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ You are free to copy, distribute and transmit the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author (but not in any way that suggests that they endorse you or your use of the work). Non Commercial: You may not use this work for commercial purposes. No Derivative Works - You may not alter, transform, or build upon this work. Any of these conditions can be waived if you receive permission from the author. Your fair dealings and other rights are in no way affected by the above. Take down policy If you believe that this document breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 07. Oct. 2021 BIG DATA APPROACHES TO INVESTIGATING CHILD MENTAL HEALTH DISORDER OUTCOMES JOHNNY DOWNS Thesis submitted for the degree of Doctor of Philosophy September 2017 Department of Psychological Medicine Institute of Psychiatry, Psychology & Neuroscience King’s College London ABSTRACT Background: In the UK, administrative data resources continue to expand across publically funded youth-orientated health, education and social services. -
Deliverable 2.4 Development of Supervised Categorization Models
Project Acronym:ICTUSnet Project code: SOE2/P1/E0623 E2.4 DEVELOPMENT OF SUPERVISED CATEGORIZATION MODELS, TOPIC MODELLING AND EXTRACTION OF CLINICAL INFORMATION COMPUTING. Due date: 30/04/2020 WP2. WPActual Development submission date: and30/ 04integration/2021 of 'Machine Responsible partner: BSC Version: Learning' algorithms04 Status: Final Dissemination level: Public / Consortium Project funded by the Interreg Sudoe Programme through the European Regional Development Fund (ERDF) Project Acronym: ICTUSnet Project code: SOE2/P1/E0623 Deliverable description: This deliverable describes the methodology used in WP2 to develop the SUPERVISED CATEGORIZATION MODELS, TOPIC MODELLING AND EXTRACTION OF CLINICAL INFORMATION using deep learning techniques. The results obtained by the deep learning models are remarkable, reaching 91% F1 on average. Revision history Version Date Comments Partner 01 12/2020 First version BSC 02 02/2021 Second version BSC 03 04/2021 Final version BSC Authors Name Partner Marta Villegas Montserrat BSC Aitor González Agirre BSC Joan Llop BSC Siamak Barzegar BSC Contributors Name Partner ICTUSnet: E2.4Development of supervised categorization models, topic modelling and extraction of clinical information via cognitive computing. 30/04/2021 02 Page 2 of 65 Project Acronym: ICTUSnet Project code: SOE2/P1/E0623 ABBREVIATIONS AND ACRONYMS HUSE Hospital Universitario Son Espases XML Extensible Markup Language HER Electronic Health Record TTR Type Token Ratio BRAT Brat Rapid Annotation Tool F1 F1 score IAA Inter-Annotator Agreement NER Named Entity Recognition NERC Named Entity Recognition and Classification ICTUSnet: E2.4Development of supervised categorization models, topic modelling and extraction of clinical information via cognitive computing. 30/04/2021 02 Page 3 of 65 Project Acronym: ICTUSnet Project code: SOE2/P1/E0623 TABLE OF CONTENTS INRODUCTION ............................................................................................................. -
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
Appendix to the paper "Code smell prediction employing machine learning meets emerging Java language constructs" Hanna Grodzicka, Michał Kawa, Zofia Łakomiak, Arkadiusz Ziobrowski, Lech Madeyski (B) The Appendix includes two tables containing the dataset used in the paper "Code smell prediction employing machine learning meets emerging Java lan- guage constructs". The first table contains information about 792 projects selected for R package reproducer [Madeyski and Kitchenham(2019)]. Projects were the base dataset for cre- ating the dataset used in the study (Table I). The second table contains information about 281 projects filtered by Java version from build tool Maven (Table II) which were directly used in the paper. TABLE I: Base projects used to create the new dataset # Orgasation Project name GitHub link Commit hash Build tool Java version 1 adobe aem-core-wcm- www.github.com/adobe/ 1d1f1d70844c9e07cd694f028e87f85d926aba94 other or lack of unknown components aem-core-wcm-components 2 adobe S3Mock www.github.com/adobe/ 5aa299c2b6d0f0fd00f8d03fda560502270afb82 MAVEN 8 S3Mock 3 alexa alexa-skills- www.github.com/alexa/ bf1e9ccc50d1f3f8408f887f70197ee288fd4bd9 MAVEN 8 kit-sdk-for- alexa-skills-kit-sdk- java for-java 4 alibaba ARouter www.github.com/alibaba/ 93b328569bbdbf75e4aa87f0ecf48c69600591b2 GRADLE unknown ARouter 5 alibaba atlas www.github.com/alibaba/ e8c7b3f1ff14b2a1df64321c6992b796cae7d732 GRADLE unknown atlas 6 alibaba canal www.github.com/alibaba/ 08167c95c767fd3c9879584c0230820a8476a7a7 MAVEN 7 canal 7 alibaba cobar www.github.com/alibaba/ -
Proceedings of the Workshop on NLP for Medicine and Biology Associated with RANLP 2013, Pages 1–8, Hissar, Bulgaria, 13 September 2013
NLP for Medicine and Biology 2013 Proceedings of the Workshop on NLP for Medicine and Biology associated with The 9th International Conference on Recent Advances in Natural Language Processing (RANLP 2013) 13 September, 2013 Hissar, Bulgaria WORKSHOP ON NLP FOR MEDICINE AND BIOLOGY ASSOCIATED WITH THE INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING’2013 PROCEEDINGS Hissar, Bulgaria 13 September 2013 ISBN 978-954-452-024-3 Designed and Printed by INCOMA Ltd. Shoumen, BULGARIA ii Preface Biomedical NLP deals with the processing of healthcare-related text—clinical documents created by physicians and other healthcare providers at the point of care, scientific publications in the areas of biology and medicine, and consumer healthcare text such as social media blogs. Recent years have seen dramatic changes in the types and amount of data available to researchers in this field. Where most research on publications in the past has dealt with the abstracts of journal articles, we now have access to the full texts of journal articles via PubMedCentral. Where research on clinical documents has been hampered by a lack of availability of data, we now have access to large bodies of data through the auspices of the Cincinnati Children’s Hospital NLP Challenge, the i2b2 shared tasks (www.i2b2.org), the TREC Electronic Medical Records track, the US-funded Strategic Health Advanced Research Projects Area 4 (www.sharpn.org) and the Shared Annotated Resources (ShARe; https://sites.google.com/site/shareclefehealth/taskdescription; www.clinicalnlpannotations.org) project. Meanwhile, the number of abstracts in PubMed continues to grow exponentially. Text in the form of blogs created by patients discussing various healthcare topics has emerged as another data source, with a new perspective on healthrelated issues. -
Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning
JMIR MEDICAL INFORMATICS Pfaff et al Viewpoint Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning Emily R Pfaff1, MS; Miles Crosskey2, PhD; Kenneth Morton2, PhD; Ashok Krishnamurthy3, PhD 1North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States 2CoVar Applied Technologies, Durham, NC, United States 3Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States Corresponding Author: Emily R Pfaff, MS North Carolina Translational and Clinical Sciences Institute University of North Carolina at Chapel Hill 160 N Medical Drive Chapel Hill, NC, United States Phone: 1 919 843 4712 Email: [email protected] Abstract Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient's medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise. Our goal was to develop an open-source phenotyping software, Clinical Annotation Research Kit (CLARK), that would enable clinical and translational researchers to use machine learning±based NLP for computable phenotyping without requiring deep informatics expertise. CLARK enables nonexpert users to mine text using machine learning classifiers by specifying features for the software to match in clinical notes. Once the features are defined, the user-friendly CLARK interface allows the user to choose from a variety of standard machine learning algorithms (linear support vector machine, Gaussian Naïve Bayes, decision tree, and random forest), cross-validation methods, and the number of folds (cross-validation splits) to be used in evaluation of the classifier. -
Interop.Jar Activation
Resource name License name License reference Usage Type a-j-interop.jar Eclipse 1.0 http://www.j-interop.org/license.html Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/javax/activation/act activation-1.1.jar CDDL 1.0 ivation/1.1/activation-1.1.pom Dynamic library activation.jar LGPL 2.1 https://github.com/wildfly/wildfly/blob/master/README.md Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/org/apache/activem activemq-all-5.7.0.jar Apache 2.0 q/activemq-all/5.7.0/activemq-all-5.7.0.pom Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/org/apache/activem activemq-core-5.7.0.jar Apache 2.0 q/activemq-core/5.7.0/activemq-core-5.7.0.pom Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/org/apache/activem activemq-pool-5.7.0.jar Apache 2.0 q/activemq-pool/5.7.0/activemq-pool-5.7.0.pom Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/org/apache/activem activemq-protobuf-1.1.jar Apache 2.0 q/protobuf/activemq-protobuf/1.1/activemq-protobuf-1.1.pom Dynamic library http://mirrors.ibiblio.org/pub/mirrors/maven2/org/apache/axis2/a addressing-1.6.2.jar Apache 2.0 ddressing/1.6.2/addressing-1.6.2.pom Dynamic library advancedPersistentLookupLib-1.0.jar Commercial http://www.talend.com Dynamic library aether-api-1.11.jar Eclipse 1.0 https://www.eclipse.org/aether/download/ Dynamic library aether-connector-asynchttpclient-1.11.jar Eclipse 1.0 https://www.eclipse.org/aether/download/ Dynamic library aether-connector-wagon-1.11.jar Eclipse 1.0 https://www.eclipse.org/aether/download/ Dynamic -
Domain Independent Natural Language Processing—A Case Study for Hospital Readmission with COPD
2014 IEEE 14th International Conference on Bioinformatics and Bioengineering Domain Independent Natural Language Processing – A Case Study for Hospital Readmission with COPD Ankur Agarwal, Ph.D. Sirish Malpura Florida Atlantic University Florida Atlantic University Boca Raton, Florida Boca Raton, Florida [email protected] [email protected] Ravi S Behara, Ph.D. Vivek Tyagi Florida Atlantic University Florida Atlantic University Boca Raton, Florida Boca Raton, Florida [email protected] [email protected] Abstract—Natural language processing is a field of computer science, which focuses on interactions between computers and phenomena based on actual examples of these phenomena human (natural) languages. The human languages are provided by the text input without adding significant linguistic ambiguous unlike Computer languages, which make its analysis or world knowledge. Machine learning based NLP solutions and processing difficult. Most of the data present these days is in use this approach. unstructured form (such as: Accident reports, Patient discharge summary, Criminal records etc), which makes it hard for Connectionist Approach: Connectionist approach computers to understand for further use and analysis. This develops generalized models from examples of linguistic unstructured text needs to be converted into structured form by phenomena. However, connectionist models combine clearly defining the sentence boundaries, word boundaries and statistical learning with various theories of representation – context dependent character boundaries for further analysis. This paper proposes a component-based domain-independent thus the connectionist representations allow transformation, text analysis system for processing of the natural language known inference, and manipulation of logic formulae. [1] as Domain-independent Natural Language Processing System (DINLP). Further the paper discusses the system capability and Connectionist NLP approach is newer compared to its application in the area of bioinformatics through the case symbolic and statistical approaches. -
Clinical Concept Extraction for Document-Level Coding
Clinical Concept Extraction for Document-Level Coding Sarah Wiegreffe1, Edward Choi1∗, Sherry Yan2, Jimeng Sun1, Jacob Eisenstein1 1Georgia Institute of Technology 2Sutter Health ∗ Current Affiliation: Google Inc [email protected], [email protected], [email protected], [email protected], [email protected] Abstract Clinical concept extraction tools abstract over The text of clinical notes can be a valuable the noise inherent in surface representations of source of patient information and clinical as- clinical text by linking raw text to standardized sessments. Historically, the primary approach concepts in clinical ontologies. The Apache clin- for exploiting clinical notes has been informa- ical Text Analysis Knowledge Extraction System tion extraction: linking spans of text to con- (cTAKES, Savova et al., 2010) is the most widely- cepts in a detailed domain ontology. How- used such tool, with over 1000 citations. Based on ever, recent work has demonstrated the poten- rules and non-neural machine learning methods and tial of supervised machine learning to extract engineered for almost a decade, cTAKES provides document-level codes directly from the raw text of clinical notes. We propose to bridge an easily-obtainable source of human-encoded do- the gap between the two approaches with two main knowledge, although it cannot leverage deep novel syntheses: (1) treating extracted con- learning to make document-level predictions. cepts as features, which are used to supple- Our goal in this paper is to maximize the predic- ment or replace the text of the note; (2) treating tive power of clinical notes by bridging the gap labels extracted concepts as , which are used to between information extraction and deep learn- learn a better representation of the text.