Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet

碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 99 === In this paper, we develop a new way to calculate the semantic realatedness between two terms. We propose a new semantic network, WikiRelationNet, and a new way to calculate semantic relatedness on it. The WikiRelationNet is built based on the entities and links...

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Main Authors: You-wei Chen, 陳佑瑋
Other Authors: Shih-Hung Wu
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/85757964031977392897
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spelling ndltd-TW-099CYUT53920062015-10-30T04:05:40Z http://ndltd.ncl.edu.tw/handle/85757964031977392897 Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet 基於維基關聯網路使用絕對深度與連結含量自動計算語意關聯性 You-wei Chen 陳佑瑋 碩士 朝陽科技大學 資訊工程系碩士班 99 In this paper, we develop a new way to calculate the semantic realatedness between two terms. We propose a new semantic network, WikiRelationNet, and a new way to calculate semantic relatedness on it. The WikiRelationNet is built based on the entities and links between entities in Wikipedia. An absolute depth concept is also proposed to covert a network graph into a tree-like graph. By measuring the distance and link content of the nodes on WikiRelationNet, we can get the semantic relatedness between two nodes. Our system is evaluated on a manually collected test set, WordSimilarity-353 collection, and the Spearman correlation coefficient between the results of our system and the manual annotated data is reported. We conduct several experiments and show how to calculate the semantic relatedness on WikiRelationNet. Shih-Hung Wu 吳世弘 2011 學位論文 ; thesis 54 zh-TW
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language zh-TW
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sources NDLTD
description 碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 99 === In this paper, we develop a new way to calculate the semantic realatedness between two terms. We propose a new semantic network, WikiRelationNet, and a new way to calculate semantic relatedness on it. The WikiRelationNet is built based on the entities and links between entities in Wikipedia. An absolute depth concept is also proposed to covert a network graph into a tree-like graph. By measuring the distance and link content of the nodes on WikiRelationNet, we can get the semantic relatedness between two nodes. Our system is evaluated on a manually collected test set, WordSimilarity-353 collection, and the Spearman correlation coefficient between the results of our system and the manual annotated data is reported. We conduct several experiments and show how to calculate the semantic relatedness on WikiRelationNet.
author2 Shih-Hung Wu
author_facet Shih-Hung Wu
You-wei Chen
陳佑瑋
author You-wei Chen
陳佑瑋
spellingShingle You-wei Chen
陳佑瑋
Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
author_sort You-wei Chen
title Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
title_short Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
title_full Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
title_fullStr Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
title_full_unstemmed Automatic Computing Semantic Relatedness Using Absolute Depth and Link Content on WikiRelationNet
title_sort automatic computing semantic relatedness using absolute depth and link content on wikirelationnet
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/85757964031977392897
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