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Extracting Named Entities Using Support Vector Machines [chapter]

Yu-Chieh Wu, Teng-Kai Fan, Yue-Shi Lee, Show-Jane Yen
2006 Lecture Notes in Computer Science  
George -person name. 5 -quantity. Ferrari -company name. 2012 -year. / 27 Named Entity Recognition (NER) Support Vector Machine (SVM) NER Using SVM What is NER?  ...  Named Entity Recognition (NER) Support Vector Machine (SVM) NER Using SVM What is NER? Enter: Name Entity Recognition "Locate and classify atomic elements in text into predefined categories."  ... 
doi:10.1007/11683568_8 fatcat:k4wzfh2ifrdljms5rxt4un24b4

Randomized Kernel Approach for Named Entity Recognition in Tamil

N. Abinaya, M. Anand Kumar, K. P. Soman
2015 Indian Journal of Science and Technology  
The NER system is also implemented using Support Vector Machine (SVM) and Conditional Random Field (CRF).  ...  A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches.  ...  Support Vector Machine Support Vector Machine (SVM) is a supervised learning algorithm with the given set of examples (inputs) with their labels (outputs).  ... 
doi:10.17485/ijst/2015/v8i24/85350 fatcat:hfky27ql7jfwvkm3x2yo7ayyga

Name Entity Recognition by New Framework Using Machine Learning Algorithm

Daljit Kaur, Ashish Verma
2014 IOSR Journal of Computer Engineering  
Our approach makes use of English contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type.  ...  Which use Hybrid approach of NlP and Machine Learning. This paper is a Review paper and Introduce Our Methodlogy.  ...  Support Vector Machine have been developed.  ... 
doi:10.9790/0661-16546671 fatcat:z2if4lmy6val3f7d4q6fin32s4

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.  ...  The aim of this study is to discuss about the methods used to recognize Biological entities like genes, proteins and diseases etc., and propose an effective method to recognize heterogeneous entities.  ...  S.Pakhomov et al [21] reported that Support Vector Machine (SVM) outperforms Maximum Entropy for Biological Named Entity Recognition.  ... 
doi:10.18535/ijecs/v6i5.57 fatcat:upi7t3tbg5hllav5g2e57qgd2a

Named Entity Recognition for Nepali Text Using Support Vector Machines

Surya Bahadur Bam, Tej Bahadur Shahi
2014 Intelligent Information Management  
The Support Vector Machine based Named Entity Recognition is limited to use a certain set of features and it uses a small dictionary which affects its performance.  ...  In this paper, Named Entity Recognition for Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning approaches for the classification task.  ...  Figure 1 . 1 Example of Named Entity Tagger. son name. Figure 2 . 2 Support Vector Machine. Figure 3 . 3 Implementation Model for Nepali NER. पोखरा <LOC> गयो <O> ……….. ………...  ... 
doi:10.4236/iim.2014.62004 fatcat:llioli7pu5c5tba67nhc23rl7i

Machine learning model for clinical named entity recognition

Ravikumar J., Ramakanth Kumar P.
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER).  ...  In this work, machine learning based approach has been used to extract the clinical data in a required manner  ...  It can be observed from the literature that various machine learners have been used. support vector machines (SVMs) [1] and hidden markov model (HMM) [2] are examples of such learners.  ... 
doi:10.11591/ijece.v11i2.pp1689-1696 fatcat:shri7ovzt5gopgduee3wdxn56i

A framework for named entity recognition of clinical data

Ravikumar J, Ramakanth Kumar P
2020 Indonesian Journal of Electrical Engineering and Computer Science  
Information extraction is one of information mining systems used to concentrate models portraying essential information classes.  ...  name and symptoms.  ...  For concept extraction, the researcher used Conditional Random Fields, and Support Vector Machines for assertion and relation annotation.  ... 
doi:10.11591/ijeecs.v18.i2.pp946-952 fatcat:pbifpjmfxjfbnoamiauvwpd4qu

Constructing Bi-order-Transformer-CRF with Neural Cosine Similarity Function for power metering entity recognition

Kaihong Zheng, Jingfeng Yang, Lukun Zeng, Qihang Gong, Sheng Li, Shangli Zhou
2021 IEEE Access  
Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF.  ...  However, the existing machine learning models do not fully consider the situation that some power metering entities' names are partially overlapping and boundaries of some power metering entities are fuzzy  ...  ACKNOWLEDGMENT This work is supported by China Southern Power Grid Co., Ltd. and Digital Grid Research Institute, China Southern Power Grid.  ... 
doi:10.1109/access.2021.3112541 fatcat:3yl5cjtpi5g6naiqswttxrznzu

Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach

Mona Soliman Habib, Jugal Kalita
2010 International Journal of Bioinformatics Research and Applications  
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and presents two implementations  ...  The training time of multi-class SVM is reduced by several orders of magnitude, which would make support vector machines a more viable and practical machine learning solution for real-world problems with  ...  BASELINE EXPERIMENTS Our baseline experiment [8] aims to identify biomedical named entities using Support Vector Machines (SVM) [28] , due to their generalization capability and their ability to handle  ... 
doi:10.1504/ijbra.2010.032121 pmid:20223740 fatcat:psvav2u7wvaujbu2shi4l673ly

Named Entity Recognition Using Support Vector Machine for Filipino Text Documents

Jonalyn Castillo, Marck Augustus L. Mateo, Antonio D. C. Paras, Ria A. Sagum, Vina Danica F. Santos
2013 International Journal of Future Computer and Communication  
Based from the results, the named entity recognizer using support vector machine performed best in tagging named entity class date with 95.52% f-measure, achieving 84.97% overall f-measure.  ...  In this study, a system for a named entity recognizer for Filipino texts using support vector machine was developed, and its performance was evaluated and compared to an existing named entity recognizer  ...  Entity Recognition Using Support Vector Machine for Filipino Text Documents Jonalyn M.  ... 
doi:10.7763/ijfcc.2013.v2.220 fatcat:bj2w7y4firetvfdfnzbcwxwauu

novel deep neural network framework for biomedical named entity recognition

Adyasha Dash, Manjusha Pandey, Siddharth Swarup Rautaray
2022 International Journal of Health Sciences  
The state-of-art systems previously adopted various supervised machine learning methods Hidden Markov Models (HMMs), Maximum Entropy Markov Models (MEMMs), Support vector machines(SVM),Structural Support  ...  Biomedical Named Entity Recognition (BNER) gets more and more attention from the researchers since it is a fundamental task in biomedical information extraction.  ...  learning based Methods Machine learning based approach makes use of a function or classification rule or classifier to detect the biomedical named entity boundaries.It classifies the named entities into  ... 
doi:10.53730/ijhs.v6ns5.9557 fatcat:pzmgdcgg7fcploqwjz7otidi5q

Developing classification-based named entity recognizers (NER) for Sambalpuri and Odia applying support vector machines (SVM)

Pitambar Behera, Sharmin Muzaffar
2018 Nepalese Linguistics  
This paper demonstrates the development of named Entity Recognizers (NER) applying Support Vector Machines (SVM) for Sambalpuri and Odia.  ...  The Sambalpuri corpus amounts to 112k word tokens out of which 5,887 are named entities. On the contrary, 250k ILCI corpus has been applied for Odia out of which 18,447 tokens are named entities.  ...  We also acknowledge the ILCI Project for providing us data for Odia applied in this current research.  ... 
doi:10.3126/nl.v33i1.41066 fatcat:domjsbw7wbdkjc5ht44kqchpdi

Addressing Scalability Issues Of Named Entity Recognition Using Multi-Class Support Vector Machines

Mona Soliman Habib
2008 Zenodo  
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features.  ...  Improving training time of multi-class SVM would make support vector machines a more viable and practical machine learning solution for real-world problems with large datasets.  ...  In this paper, a series of experiments is presented in order to explore the scalability issues associated with solving the named entity recognition problem using multi-class support vector machines and  ... 
doi:10.5281/zenodo.1078583 fatcat:jea3vravl5g7tax5fjik7xfhxa

Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents [chapter]

Paulo Quaresma, Teresa Gonçalves
2010 Lecture Notes in Computer Science  
In this approach, top-level legal concepts are identified and used for document classification using Support Vector Machines.  ...  Named entities, such as, locations, organizations, dates, and document references, are identified using semantic information from the output of a natural language parser.  ...  In this approach, top-level legal concepts are identified and used for document classification using a well known machine learning technique -Support Vector Machines.  ... 
doi:10.1007/978-3-642-12837-0_3 fatcat:5hmwmab6tzaerdxn7wpijfjbki

Named Entity Recognizer employing Multiclass Support Vector Machines for the Development of Question Answering Systems

Bindu. M.S, Sumam Mary Idicula
2011 International Journal of Computer Applications  
The system presented here is a Named Entity (NE) Classifier created using Multiclass Support Vector Machines based on linguistic grammar principles.  ...  Named entities tell us the roles of each meaning bearing word in a sentence and hence identification of these entities certainly helps us to extract the essence of the text which is very important in Question  ...  Support Vector Machines Support vector Machines proposed by Vapnik is a set of Machine learning algorithms based on statistical methods.  ... 
doi:10.5120/3146-4343 fatcat:azhvf5ai5ratlkoklfmhm5bfju
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