Using Opinion-based Entity Ranking Model to Improve List-Informational Search
碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 101 === Natural language search is to use human language questions as query to search and extract suitable webpages for users. Compare with short query, natural language query users can directly submit their query intents. For example, the question, “Which restaura...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2013
|
Online Access: | http://ndltd.ncl.edu.tw/handle/81385383779751856433 |
id |
ndltd-TW-101NCKU5392106 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-101NCKU53921062015-10-13T22:51:45Z http://ndltd.ncl.edu.tw/handle/81385383779751856433 Using Opinion-based Entity Ranking Model to Improve List-Informational Search 運用以意見為基礎的實體排序模型改善條列式資訊檢索 Jhih-YaoHuang 黃致堯 碩士 國立成功大學 資訊工程學系碩博士班 101 Natural language search is to use human language questions as query to search and extract suitable webpages for users. Compare with short query, natural language query users can directly submit their query intents. For example, the question, “Which restaurants in Tainan have delicious hot pot?”. According to our observation, conventional search engines can’t efficiently process this queries and the search result is too messy. Therefore users need to spend lots of time on browsing and filter those noise information in the result pages . Rose et al. proposed a conceptual framework for classifying user’s goals. Such that, search engines can associate user goals with queries and exploit the goal information. In our paper, we focus in his proposed list-informational search goal. We proposed a website-base entity set expansion method to expand our entities. We try to recommend evidence pages of each entity based on the intent behind users by analyzing question structures and answer structures. The question structure can be divided into three parts, they are question focus, question context and question opinion pair. We used the algorithm of question analysis to identify question features. As to the answer structure, it can be divided into entity homepage, context evidence page and opinion summary. We combine the relationship between the question structure and the answer structure with the real comments on the Internet to construct Opinion-based Entity Ranking Model (OBERM) to improve List-Informational search. Experiment result shows that our proposed method OBERM outperforms Google Search. And it shows OBERM really can enhance performance in List-informational search. Wen-Hsiang Lu 盧文祥 2013 學位論文 ; thesis 48 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 101 === Natural language search is to use human language questions as query to search and extract suitable webpages for users. Compare with short query, natural language query users can directly submit their query intents. For example, the question, “Which restaurants in Tainan have delicious hot pot?”. According to our observation, conventional search engines can’t efficiently process this queries and the search result is too messy. Therefore users need to spend lots of time on browsing and filter those noise information in the result pages . Rose et al. proposed a conceptual framework for classifying user’s goals. Such that, search engines can associate user goals with queries and exploit the goal information. In our paper, we focus in his proposed list-informational search goal.
We proposed a website-base entity set expansion method to expand our entities. We try to recommend evidence pages of each entity based on the intent behind users by analyzing question structures and answer structures. The question structure can be divided into three parts, they are question focus, question context and question opinion pair. We used the algorithm of question analysis to identify question features. As to the answer structure, it can be divided into entity homepage, context evidence page and opinion summary. We combine the relationship between the question structure and the answer structure with the real comments on the Internet to construct Opinion-based Entity Ranking Model (OBERM) to improve List-Informational search.
Experiment result shows that our proposed method OBERM outperforms Google Search. And it shows OBERM really can enhance performance in List-informational search.
|
author2 |
Wen-Hsiang Lu |
author_facet |
Wen-Hsiang Lu Jhih-YaoHuang 黃致堯 |
author |
Jhih-YaoHuang 黃致堯 |
spellingShingle |
Jhih-YaoHuang 黃致堯 Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
author_sort |
Jhih-YaoHuang |
title |
Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
title_short |
Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
title_full |
Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
title_fullStr |
Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
title_full_unstemmed |
Using Opinion-based Entity Ranking Model to Improve List-Informational Search |
title_sort |
using opinion-based entity ranking model to improve list-informational search |
publishDate |
2013 |
url |
http://ndltd.ncl.edu.tw/handle/81385383779751856433 |
work_keys_str_mv |
AT jhihyaohuang usingopinionbasedentityrankingmodeltoimprovelistinformationalsearch AT huángzhìyáo usingopinionbasedentityrankingmodeltoimprovelistinformationalsearch AT jhihyaohuang yùnyòngyǐyìjiànwèijīchǔdeshítǐpáixùmóxínggǎishàntiáolièshìzīxùnjiǎnsuǒ AT huángzhìyáo yùnyòngyǐyìjiànwèijīchǔdeshítǐpáixùmóxínggǎishàntiáolièshìzīxùnjiǎnsuǒ |
_version_ |
1718081386754080768 |