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Biomedical Named Entity Recognition - a swift review

S. Vijaya
2017 International Journal Of Engineering And Computer Science  
The main focus of this paper is taking a swift review on the Biomedical Named Entity Recognition which is the most complex task in Information Extraction.  ...  This paper analyses various methods used for NER particularly in the field of Biomedical domain.  ...  The difficulty in applying rules defined for a particular domain to other domain , requirement of wide domain knowledge to define rules, time consumption are major drawbacks in rule based approach.[1]  ... 
doi:10.18535/ijecs/v6i5.57 fatcat:upi7t3tbg5hllav5g2e57qgd2a

Eclectic Rule-Extraction From Support Vector Machines

Nahla Barakat, Joachim Diederich
2008 Zenodo  
In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented.  ...  Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains.  ...  In case of support vectors machines, knowledge acquired during the training phase is represented by the model support vectors, and the parameters associated with them.  ... 
doi:10.5281/zenodo.1055511 fatcat:akpbjgvbj5cz3ghk4qr3kfi2om

A Hybrid Approach Based Sentiment Extraction from Medical Context

Anupam Mondal, Ranjan Satapathy, Dipankar Das, Sivaji Bandyopadhyay
2016 International Joint Conference on Artificial Intelligence  
In contrast, one of our primary motivations here is to build a sentiment extraction model based on medical contexts to leverage the knowledge of WME using a hybrid approach.  ...  Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a  ...  Introduction One of the major objectives of Sentiment Analysis is to identify and extract the subjective information from a given text using rule based or machine learning approaches [Cambria, 2016] .  ... 
dblp:conf/ijcai/MondalSDB16 fatcat:aahkhipk5vdw5mmofw6vgiwl2u

Efficient and interpretable fuzzy classifiers from data with support vector learning

Stergios Papadimitriou, Konstantinos Terzidis
2005 Intelligent Data Analysis  
After the construction of the interpretable fuzzy partitions, the developed algorithms extract from the SVFI rules a small and concise set of interpretable rules.  ...  Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance.  ...  Acknowledgment This work was partially supported from a European Union funded EPEAK II project "Arximidis", code 04-3-001/5, performed at the Technological Educational Institute of Kavalas, Dept. of Information  ... 
doi:10.3233/ida-2005-9603 fatcat:rn5ytq7t5rcvxafyiddxahk43a

Data mining for customer service support

S.C. Hui, G. Jha
2000 Information & Management  
This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis.  ...  In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: (1) unstructured customer service reports record machine  ...  The objective of this paper is to discuss how to apply data mining techniques to extract knowledge from the customer service database to support two types of activities: decision support and machine fault  ... 
doi:10.1016/s0378-7206(00)00051-3 fatcat:gxpit6fyy5bwhlqyk4qo2j3neu

Base Types Selection of Product Service System Based on Apriori Algorithm and Knowledge-based Artificial Neural Network

Zaifang Zhang, Nana Chai, Yuan Liu, Bei Xia
2019 IET Collaborative Intelligent Manufacturing  
First, the data of historical configuration instance data sets are processed and then a priori algorithm is used to extract the effective rules as domain knowledge.  ...  Domain knowledge is used to build the initial structure of ANN. Moreover, data sets are used to further optimize the network.  ...  Acknowledgments This project is supported by the National Natural Science Foundation of China (Nos. 51205242 and 71401098).  ... 
doi:10.1049/iet-cim.2018.0003 fatcat:nv737vfanbgkrgw2oclkvn7sdu

Mining Quality Phrases from Massive Text Corpora

Jialu Liu, Jingbo Shang, Chi Wang, Xiang Ren, Jiawei Han
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
Traditional data-driven approaches  Frequent pattern mining   E.g., freq(vector machine) ≥ freq(support vector machine)  Need to rectify the frequency based on segmentation results  Phrasal  ...  3 association rule knowledge discovery 4 knowledge discovery frequent itemset 5 time series decision tree … … … 51 association rule mining search space 52 rule set domain knowledge  ...  similar phrases  In DBLP, query on "data mining" and "OLAP"  In Yelp, query on "blu-ray", "noodle", and "valet parking"  ... 
doi:10.1145/2723372.2751523 pmid:26705375 pmcid:PMC4688018 dblp:conf/sigmod/LiuSWRH15 fatcat:6gme5yjdzjdzpaxdywqq7koqiy

A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction [article]

Yanshan Wang, Sunghwan Sohn, Sijia Liu, Feichen Shen, Liwei Wang, Elizabeth J. Atkinson, Shreyasee Amin, Hongfang Liu
2018 arXiv   pre-print
We evaluated the effectiveness of the proposed paradigm on two clinical information extraction tasks: smoking status extraction and proximal femur (hip) fracture extraction.  ...  We tested three prevalent machine learning models, namely, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and Random Forrest (RF).  ...  Acknowledgements This work was supported by NIA P01AG04875, NIGMS R01GM102282, NCATS U01TR002062, and NLM R01LM11934 and made possible by the Rochester Epidemiology Project (NIA R01AG034676) and the U.S  ... 
arXiv:1804.07814v1 fatcat:mbem63ckanfj3mlbogn3n2axmu

Semantic Reasoning for Smog Disaster Analysis

Jiaoyan Chen, Huajun Chen, Jeff Z. Pan
2016 International Workshop on Description Logics  
the knowledge (i.e., assertions and rules) in machine learning algorithms, and finally provided explanations by rule-based reasoning.  ...  To this end, we enriched the smog data streams with background knowledge by ontology modeling, inferred underlying knowledge like semantic assertions and rules, built consistent prediction models by embedding  ...  The rule with high confidence and support, also known as an prominent rule indicates strong predictive information and its prefixes are used to infer effective features.  ... 
dblp:conf/dlog/ChenCP16 fatcat:lzho6c4mcbcptguarjnxa342te

Web Page Classification Using SVM and FURIA

P. Madhubala, K. Murugesan
2015 Research Journal of Applied Sciences Engineering and Technology  
Performance evaluation is done using Support Vector Machine (SVM) classifier and Fuzzy Unordered Rule Induction Algorithm (FURIA) classifier.  ...  Features extraction is performed with weighted Term Frequency-Inverse Document Frequency (TF-IDF) where the weight of the word can be computed based on the number of hyponyms present in the radix tree.  ...  The machine learning algorithms such as Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DTREE) and Support Vector Machines (SVM) were used for event extraction.  ... 
doi:10.19026/rjaset.9.1434 fatcat:r67ovyamv5f2lawqyia6ddjaaa

Efficient Intelligent Generic Recommendation Knowledge Graph in Education Domain using Association Rule Mining and Machine Learning

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Knowledge graph is used to extract and derive new facts from huge variety of data sources through relationship.  ...  The main objective of the proposed graph is to organize a generic knowledge graph for deriving huge amount of new facts to the education domain with maximum support and confidence level.  ...  Knowledge Graph in Education Domain using Association Rule Mining and Machine Learning The graph is organized based on the keywords which are exists already and produces new knowledge [4] .  ... 
doi:10.35940/ijitee.j1166.0881019 fatcat:unmtpqxwircdpkxgjg54oiyveq

Text Categorization Based on Domain Ontology [chapter]

Qinming He, Ling Qiu, Guotao Zhao, Shenkang Wang
2004 Lecture Notes in Computer Science  
Methods based on machine learning have been proposed with certain advantages for TC (text categorization).  ...  Not only more effect and understandability of categorization are achieved, simulation results show a great reducing of keyword numbers and saving of system costs.  ...  With the support of domain vocabulary, the domain ontology provides related domain knowledge, including concepts, associations, entities, attributes, etc.  ... 
doi:10.1007/978-3-540-30480-7_33 fatcat:3r2fickc4ranljmchxczoam7xe

A Semantic Annotation Tool to Extract Instances from Korean Web Documents

Hai-Tao Zheng, Bo-Yeong Kang, Sang-Ok Koo, Hee-Chul Choi, Kwang-Sub Kim, Hong-Gee Kim
2006 International Semantic Web Conference  
In this paper, we propose a semantic annotation system named SARM, which has an automatic instance extraction module based on two machine learning techniques, Bayesian Classifier and Support Vector Machine  ...  We describe the implementation of our system and also compare the performances of the two machine learning methods we used.  ...  ACKNOWLEDGMENTS This research is supported by Ministry of Information and Communication Republic of KOREA -National Project (Project management of Institute for Information Technology Advancement).  ... 
dblp:conf/semweb/ZhengKKCKK06 fatcat:b36u7v24vjbutfm65pr736ugju

Classify Unexpected News Impacts to Stock Price by Incorporating Time Series Analysis into Support Vector Machine

Ting Yu, T. Jan, J. Debenham, S. Simoff
2006 The 2006 IEEE International Joint Conference on Neural Network Proceedings  
the paper discusses an approach of using traditional time series analysis, as domain knowledge, to help the data-preparation of support vector machine for classifying documents.  ...  A classifier mainly built by support vector machine uses the training data set to extract the interrelationship between unexpected news events and the stock price movements. 1 1 t t Ht t t t  ...  ACKNOWLEDGMENT The authors would like to thank Dr.  ... 
doi:10.1109/ijcnn.2006.247256 dblp:conf/ijcnn/YuJDS06 fatcat:526qi25dovcaffx76ajs5eerly

Applications of Design Patterns and Data Mining Algorithms in Software Development

2017 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
To this perspective, this paper proposes the new idea on the machine leaning based model the performance is verified in the final part. In the future, we will conduct the robustness test.  ...  Analysis model is the concept of reusable model within the territory, and design patterns in software development is in a class of problems of the general solution, often used in an analysis mode multiple  ...  Not only sparse hidden space support vector machines has surpassed support vector machines on sparse, moreover has with the support vector machines same promotion performance.  ... 
doi:10.21311/001.39.11.44 fatcat:3nltemr4ozfcniqdfbdcjspqgi
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