Dissertation > Excellent graduate degree dissertation topics show

Combination of machine learning methods named entity recognition

Author: ShiYongGang
Tutor: ZuoZhiHong
School: University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Named Entity Recognition machine learning statistics and rules decision tree algorithm
CLC: TP391.4
Type: Master's thesis
Year: 2006
Downloads: 251
Quote: 4
Read: Download Dissertation


Named Entity Recognition (NER) technologies have become a hot problem of Natural Language Process recently.The definition of Named Entity by MUC(Message Understanding Conference) is the proper nouns and the quantifiers that people are Interested in.NER can be classified to person-name,location,organization,date,number and so on.NER has been applied on many compute linguistics tasts as a subtask of Information Extraction,such as machine translation.Just as most of the Natural Language Process technologies,the methods of NER have two classes, statistic-based and rule-based.Considering of the limitation of using only one of the methods,we combined both of the methods to recognize Named Entity in this thesis .we combined the maching learning with NER to make the system get the ability of self-learning.We have done research on decision tree of maching learning mainly and designed a recognize model to recognize Named Entity.This model first used the probability and statistic way to extract the potential named entities,and then some context linguistic language information are employed in the model to recognize the named entities furtherly.As the wrong entites are denied ,the recongnize effect has been improved. By using the methods above,we mainly researched on Chinese person nameand location.The result of the experiments shows that the effort of the strategy based on rules and statistics is better than use only one of them.in the same experimental condition ,the model combined on machine learning is constructed simply , has better adaptability and self-learning ability.This thesis is mainly classified to four models .1.Text preprocessing.2.Chinese name and location recognization based on statistics and rules.3.Chinese name and location recognization combined with maching learningmethods.4.Eliminating the ambiguities of Chinese name and location.

Related Dissertations

  1. Based on Data Distribution Characteristics of Text Classification,TP391.1
  2. The Research for Named Entity Recognition and Relation Extraction in Text,TP391.1
  3. Theories and Algorithms of Manifold Learning and Applications in Biometric Authentication,TP181
  4. Sentiment Classifiation Via Sequence Modeling,TP393.09
  5. Automatically Chinese Address Recognition and Normalization,TP391.43
  6. Prediction and Feature Analyses of Multi-Functional Enzymes,TQ225.1
  7. Intrusion Detection Through Network Performance Learning,TP393.08
  8. Using Mutual Information for Selecting Continuous-valued Attribute in Decision Tree Learning,O159
  9. INTERNET users personalized interested in model - based research,TP393.09
  10. Research on Cost-Sensitive Machine Learning Based on Dynamic Cost,TP181
  11. Restricted Boltzmann Machines: a Collaborative Filtering Perspective,TP181
  12. Study on Least Square Support Vector Machine Algorithms and Their Applications,TP18
  13. Research on Protein Subcellular Localization Prediction Based on Machine Learning Methods,TP181
  14. Research on Model Selection for Machine Learning,TP181
  15. Research of the Data Mining and Analysis Based on Logistics Distribution System,TP311.13
  16. Blind Equalization Based on Support Vector Machine,TN929.3
  17. Fast classification based on the degree of interest in the decision tree algorithm optimization,TP311.13
  18. Research on Decision Tree Algorithm and Application Based on Data Mining,TP311.13
  19. NLP and ML Based Text Classification and Its Applications,TP391.1
  20. Study of Securities Investment Decision on the Basis of Support Vector Machine,F830.91

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices
© 2012 www.DissertationTopic.Net  Mobile