Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model
碩士 === 國立雲林科技大學 === 資訊管理系 === 103 === There are many researches that using machine learning to recognize Chinese name entity, but those researches used news or existing corpus. E.g. IEER-99、MET2. With different culture evolution and time passing, name rule has been changing and increasing. Maintain...
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ndltd-TW-103YUNT03960682016-07-02T04:28:42Z http://ndltd.ncl.edu.tw/handle/25020303867299358162 Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model 使用隱藏式馬可夫模型及維基百科辨識中文人名命名實體 Chen PO-Hung 陳柏宏 碩士 國立雲林科技大學 資訊管理系 103 There are many researches that using machine learning to recognize Chinese name entity, but those researches used news or existing corpus. E.g. IEER-99、MET2. With different culture evolution and time passing, name rule has been changing and increasing. Maintain the name database become big problem. Therefore, this study proposed that the use of Wikipedia for training data to recognize the Chinese name. Combining the data source which extract from the Wikipedia and Hidden Markov Model (HMM) to solve the problem. In order to validate the proposed method are valid, we carried out a two-stage evaluation method. Experimental results show that the proposed method can effectively identify different name which generate by different rule. The advantage to use Wikipedia is solving the problem which need to collect large training data and the maintenance of Wikipedia is in real time. Phase I use cross-validation and the precision, recall and rates F-Measure of the Phase I are 87%, 79% and 83%, respectively. Phase II is to identify the students name in 2012 Taiwan General Scholastic Ability Test. The precision, recall and rates F-Measure of evaluation are 95%, 91% and 93% Huang Chuen-min 黃純敏 2015 學位論文 ; thesis 30 zh-TW |
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碩士 === 國立雲林科技大學 === 資訊管理系 === 103 === There are many researches that using machine learning to recognize Chinese name entity, but those researches used news or existing corpus. E.g. IEER-99、MET2. With different culture evolution and time passing, name rule has been changing and increasing. Maintain the name database become big problem. Therefore, this study proposed that the use of Wikipedia for training data to recognize the Chinese name. Combining the data source which extract from the Wikipedia and Hidden Markov Model (HMM) to solve the problem. In order to validate the proposed method are valid, we carried out a two-stage evaluation method. Experimental results show that the proposed method can effectively identify different name which generate by different rule. The advantage to use Wikipedia is solving the problem which need to collect large training data and the maintenance of Wikipedia is in real time. Phase I use cross-validation and the precision, recall and rates F-Measure of the Phase I are 87%, 79% and 83%, respectively. Phase II is to identify the students name in 2012 Taiwan General Scholastic Ability Test. The precision, recall and rates F-Measure of evaluation are 95%, 91% and 93%
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author2 |
Huang Chuen-min |
author_facet |
Huang Chuen-min Chen PO-Hung 陳柏宏 |
author |
Chen PO-Hung 陳柏宏 |
spellingShingle |
Chen PO-Hung 陳柏宏 Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
author_sort |
Chen PO-Hung |
title |
Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
title_short |
Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
title_full |
Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
title_fullStr |
Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
title_full_unstemmed |
Chinese Person Named Entity Recognition Using Wikipedia and Hidden Markov Model |
title_sort |
chinese person named entity recognition using wikipedia and hidden markov model |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/25020303867299358162 |
work_keys_str_mv |
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