Corpus-Based Coherence Relation Tagging in Chinese Discourse
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Discourse analysis plays an important role of document understanding and is crucial for clarifying the proposition and logical structure of the document. Therefore, this thesis is aimed to built a automated Chinese discourse tagging system by collecting and ex...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2006
|
Online Access: | http://ndltd.ncl.edu.tw/handle/96375712768665040446 |
id |
ndltd-TW-094NCTU5394061 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-094NCTU53940612016-05-27T04:18:35Z http://ndltd.ncl.edu.tw/handle/96375712768665040446 Corpus-Based Coherence Relation Tagging in Chinese Discourse 以語料為基礎的中文語篇連貫關係自動標記 Shou-Yi Cheng 鄭守益 碩士 國立交通大學 資訊科學與工程研究所 94 Discourse analysis plays an important role of document understanding and is crucial for clarifying the proposition and logical structure of the document. Therefore, this thesis is aimed to built a automated Chinese discourse tagging system by collecting and expanding the coherence feature of discourse base on corpus study and to design the corresponding rules. We used the written documents from Sinica Balance Corpus 3.0 as our mining corpus. It includes 7265 articles covering news, biographies, essays, letters, commentary and illustration manuals. We mine individually cue term, continuous POS tag and peculiar punctuation marks for nine types of rhetorical relations of Chinese discourse, that includes Coordinate, Continue, Option, Forward, Disjunctive, Cause and Effect, Conditions, Elaboration and Goal. In our experiment, we used 100 news editorial articles, each of which contains around 1500 words(1424~1558), as testing corpus. The precision, recall and filtration precision of intra sentence tagging achieve 91%, 95% and 98%. On the other hand, the precision, recall and filtration precision of inter sentence tagging achieve 86%, 93% and 95%. Tyne Liang 梁婷 2006 學位論文 ; thesis 62 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Discourse analysis plays an important role of document understanding and is crucial for clarifying the proposition and logical structure of the document. Therefore, this thesis is aimed to built a automated Chinese discourse tagging system by collecting and expanding the coherence feature of discourse base on corpus study and to design the corresponding rules. We used the written documents from Sinica Balance Corpus 3.0 as our mining corpus. It includes 7265 articles covering news, biographies, essays, letters, commentary and illustration manuals. We mine individually cue term, continuous POS tag and peculiar punctuation marks for nine types of rhetorical relations of Chinese discourse, that includes Coordinate, Continue, Option, Forward, Disjunctive, Cause and Effect, Conditions, Elaboration and Goal. In our experiment, we used 100 news editorial articles, each of which contains around 1500 words(1424~1558), as testing corpus. The precision, recall and filtration precision of intra sentence tagging achieve 91%, 95% and 98%. On the other hand, the precision, recall and filtration precision of inter sentence tagging achieve 86%, 93% and 95%.
|
author2 |
Tyne Liang |
author_facet |
Tyne Liang Shou-Yi Cheng 鄭守益 |
author |
Shou-Yi Cheng 鄭守益 |
spellingShingle |
Shou-Yi Cheng 鄭守益 Corpus-Based Coherence Relation Tagging in Chinese Discourse |
author_sort |
Shou-Yi Cheng |
title |
Corpus-Based Coherence Relation Tagging in Chinese Discourse |
title_short |
Corpus-Based Coherence Relation Tagging in Chinese Discourse |
title_full |
Corpus-Based Coherence Relation Tagging in Chinese Discourse |
title_fullStr |
Corpus-Based Coherence Relation Tagging in Chinese Discourse |
title_full_unstemmed |
Corpus-Based Coherence Relation Tagging in Chinese Discourse |
title_sort |
corpus-based coherence relation tagging in chinese discourse |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/96375712768665040446 |
work_keys_str_mv |
AT shouyicheng corpusbasedcoherencerelationtagginginchinesediscourse AT zhèngshǒuyì corpusbasedcoherencerelationtagginginchinesediscourse AT shouyicheng yǐyǔliàowèijīchǔdezhōngwényǔpiānliánguànguānxìzìdòngbiāojì AT zhèngshǒuyì yǐyǔliàowèijīchǔdezhōngwényǔpiānliánguànguānxìzìdòngbiāojì |
_version_ |
1718282640884236288 |