Extracting Scales of Measurement Automatically from Biomedical Text with Special Emphasis on Comparative and Superlative Scales

Abstract In this thesis, the focus is on the topic of “Extracting Scales of Measurement Automatically from Biomedical Text with Special Emphasis on Comparative and Superlative Scales.” Comparison sentences, when considered as a critical part of scales of measurement, play a highly significant role in the process of gathering information from a large number of biomedical research papers. A comparison sentence is defined as any sentence that contains two or more entities that are being compared. This thesis discusses several different types of comparison sentences such as gradable comparisons and non-gradable comparisons. The main goal is extracting comparison sentences automatically from the full text of biomedical articles. Therefore, the thesis presents a Java program that could be used to analyze biomedical text to identify comparison sentences by matching the sentences in the text to 37 syntactic and semantic features. These features or qualities would be helpful to extract comparative sentences from any biomedical text. Two machine learning techniques are used with the 37 roles to assess the curated dataset. The results of this study are compared with earlier studies

The Business Impact of Social Media - Sentiment Analysis Approach -

이 연구의 목적은 소셜 미디어에서 추출된 7개의 감성 도메인이 자동차 시장 점유율 예측에 대한 감성 분석 실험을 위한 데이터로서 적합한 지에 대한 신뢰성을 확인하고 고객들의 의견이 기업의 성과에 어떻게 영향을 미치는 지에 대하여 확인하기 위한 것이다. 본 연구는 총3단계에 걸쳐서 진행되었습니다. 첫 번째 단계는 감성사전 구축의 단계로서 2013년 1월 1일부터 2015년 12월 31일까지 미국 내 26개의 자동차 제조 회사의 고객의 소리 (VOC: Voice of the Customer) 총 45,447개를 자동차 커뮤니티로부터 크롤링 (crawling)하여 POS (Part-of-Speech) 즉 품사정보를 추출하는 태깅 (tagging)과정을 거쳐 부정적, 긍정적 감성의 빈도수를 측정하여 감성사전을 구축하였고, 이에 대한 극성을 측정하여 7개의 감성도메인을 만들었습니다. 두 번째 단계는 데이터에 대한 신뢰성 분석의 단계로서 자기상관관계분석 (Auto-correlation Analysis)과 주성분분석 (PCA: Principal Component Analysis)을 통해 데이터가 실험에 적합한지를 검증하였다. 세 번째 단계에서는 2개의 선형회귀분석 모델로 7개의 감성영역이 미국내 자동차 제조 회사 중 GM, 포드, FCA, 폭스바겐 등 총 4개의 자동차 생산 기업을 선정하여 이들 기업의 성과 즉, 자동차 시장점유율에 어떤 영향을 미치고 있는 지 실험하였다. 그 결과, 우리는 4,815개의 부정적인 어휘들과 2,021개의 긍정적인 감성어휘들을 추출하여 감성사전을 구축하였으며, 구축된 감성사전을 바탕으로, 추출되고 분류된 부정적이고 긍정적인 어휘들을 자동차 산업에 관련된 어휘들과 조합하였고, 자기상관분석과 PCA (주성분 분석)를 통해 감성의 특성을 조사하였다. 실험 결과에 따르면, 자기상관분석에 의해서 감성 데이터에 어떤 일정한 패턴이 존재한다는 것이 발견되었고, 각각의 감성 영역의 감성이 자기상관성이 있으며, 감성의 시계열성 또한 관찰되었다. PCA에 의한 결과로서, 7개 감성영역이 부정성, 긍정성, 중립성을 주성분으로 연결되어 있음을 확인할 수 있었다. 자기상관분석과 PCA를 통한 VOC 감성 데이터에 대한 신뢰성을 바탕으로 2개의 선형회귀분석 모델을 구축하여 실험을 진행하였다. 첫 번째 모델은 주성분 분석에서 부정적 감성의 Sadness, Anger, Fear와 긍정적 감성도메인인 Delight, Satisfaction을 독립변수로 선정하고, 시장점유율을 종속변수로 선정하여 실행하였고 두 번째 모델은 첫 번째 모델에 주성분이 중립성으로 결과가 나온 Shame, Frustration을 독립변수에 추가하여 중립성을 띠고 있는 감성이 시장 점유율에 유의미한 영향을 미치고 있는 지를 확인하였다. 분석 결과, 각 기업 마다 시장점유율에 유의미한 영향을 미치는 감성들이 존재하고 모델 1과, 모델 2에서의 감성 영향력이 차이가 있음을 발견하였다. 본 연구를 통해, 데이터 상에 나타난 정보를 가진 감성이 과거 값에 기초하여 자동차 시장에서 변화를 수반할 수 있다는 것을 나타내고 있음을 확인하였다. 또한, 우리가 시장 데이터의 가용성을 적용하려고 할 때, 자동차 시장 관련 정보나 감성의 자기상관성을 잘 활용할 수 있다면, 감정 분석에 대한 연구에 큰 기여를 할 수 있을 뿐만 아니라, 실제 시장에서의 비지니스 성과에도 다양한 방법으로 기여할 수 있을 것으로 기대된다.List of Tables iv List of Figures v Abstract 1 1. Introduction 1.1 Back Ground 3 1.2 Necessity of Study 6 1.3 Purpose & Questions 8 1.4 Structure 9 2. Literature Reviews of VOC Analysis 2.1 Importance of VOC 11 2.2 Data Mining 15 2.2.1 Concept & Functionalities 15 2.2.2 Methodologies of Data mining 20 2.3 Text Mining 24 2.4 Sentiment Analysis 26 2.5 Research Trend in Korea 30 3. Methodology 3.1 Research Flow 32 3.2 Proposed Methodologies 34 3.2.1 Sentiment Analysis 34 3.2.2 Auto-correlation Analysis 37 3.2.3 Principal Component Analysis (PCA) 38 3.2.4 Linear Regression 40 4. Experiment & Analysis 4.1 Phase I: Constructing Sentiment Lexicon & 7 Sentiment Domains 43 4.1.1 The Subject of Analysis & Crawling Data 43 4.1.2 Extracting POS Information 44 4.1.3 Review Extracting POS Information 46 4.2 Phase II : Reliability Analysis 49 4.2.1 Auto-correlation Analysis of Sentiment 51 4.2.2 Principal Component Analysis of Sentiment 55 4.3 Phase III : Influence on Automotive Market Share 58 4.3.1 Linear Regression Model 58 4.3.2 Definition of Variables 60 4.3.3 The Result of Linear Regression Analysis 62 5. Conclusion 5.1 Summary of Study 73 5.2 Managerial Implication and Limitation 75 5.3 Future Study 77 References 79Docto

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

بناء أداة تفاعلية متعددة اللغات لاسترجاع المعلومات

The growing requirement on the Internet have made users access to the information expressed in a language other than their own , which led to Cross lingual information retrieval (CLIR) .CLIR is established as a major topic in Information Retrieval (IR). One approach to CLIR uses different methods of translation to translate queries to documents and indexes in other languages. As queries submitted to search engines suffer lack of untranslatable query keys (i.e., words that the dictionary is missing) and translation ambiguity, which means difficulty in choosing between alternatives of translation. Our approach in this thesis is to build and develop the software tool (MORTAJA-IR-TOOL) , a new tool for retrieving information using programming JAVA language with JDK 1.6. This tool has many features, which is develop multiple systematic languages system to be use as a basis for translation when using CLIR, as well as the process of stemming the words entered in the query process as a stage preceding the translation process. The evaluation of the proposed methodology translator of the query comparing it with the basic translation that uses readable dictionary automatically the percentage of improvement is 8.96%. The evaluation of the impact of the process of stemming the words entered in the query on the quality of the output process in the retrieval of matched data in other process the rate of improvement is 4.14%. Finally the rated output of the merger between the use of stemming methodology proposed and translation process (MORTAJA-IR-TOOL) which concluded that the proportion of advanced in the process of improvement in data rate of retrieval is 15.86%. Keywords: Cross lingual information retrieval, CLIR, Information Retrieval, IR, Translation, stemming.الاحتياجات المتنامية على شبكة الإنترنت جعلت المستخدمين لهم حق الوصول إلى المعلومات بلغة غير لغتهم الاصلية، مما يقودنا الى مصطلح عبور اللغات لاسترجاع المعلومات (CLIR). CLIR أنشئت كموضوع رئيسي في "استرجاع المعلومات" (IR). نهج واحد ل CLIR يستخدم أساليب مختلفة للترجمة ومنها لترجمة الاستعلامات وترجمة الوثائق والفهارس في لغات أخرى. الاستفسارات والاستعلامات المقدمة لمحركات البحث تعاني من عدم وجود ترجمه لمفاتيح الاستعلام (أي أن العبارة مفقودة من القاموس) وايضا تعاني من غموض الترجمة، مما يعني صعوبة في الاختيار بين بدائل الترجمة. في نهجنا في هذه الاطروحة تم بناء وتطوير الأداة البرمجية (MORTAJA-IR-TOOL) أداة جديدة لاسترجاع المعلومات باستخدام لغة البرمجة JAVA مع JDK 1.6، وتمتلك هذه الأداة العديد من الميزات، حيث تم تطوير منظومة منهجية متعددة اللغات لاستخدامها كأساس للترجمة عند استخدام CLIR، وكذلك عملية تجذير للكلمات المدخلة في عملية الاستعلام كمرحلة تسبق عملية الترجمة. وتم تقييم الترجمة المنهجية المقترحة للاستعلام ومقارنتها مع الترجمة الأساسية التي تستخدم قاموس مقروء اليا كأساس للترجمة في تجربة تركز على المستخدم وكانت نسبة التحسين 8.96% , وكذلك يتم تقييم مدى تأثير عملية تجذير الكلمات المدخلة في عملية الاستعلام على جودة المخرجات في عملية استرجاع البيانات المتطابقة باللغة الاخرى وكانت نسبة التحسين 4.14% , وفي النهاية تم تقييم ناتج عملية الدمج بين استخدام التجذير والترجمة المنهجية المقترحة (MORTAJA-IR-TOOL) والتي خلصت الى نسبة متقدمة في عملية التحسين في نسبة البيانات المرجعة وكانت 15.86%

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio