Development of Language Information System and Systematic Extraction of Latent Knowledge, Based on Graph Computation of Semantic Network

基于语义网络图计算的语言信息系统开发和潜在知识的系统提取

• 批准号: 18500192
• 负责人: AKAMA Hiroyuki
• 金额: $ 2.05万
• 依托单位: Tokyo Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500192/
• 关键词: Semantic Network; Graph Theory; Latent Knowledge; Educational Information System; Hidden Knowledge

We developed a new method of solving the cluster-size imbalance problem observed when documents and corpora are processed with MCL. The Branching MCL (BMCL) or the latent adjacency matrix can resize overly inclusive Markov clusters (core clusters) into appropriate subsets. This method is applied to a semantic network built from the large-scale corpus of Gakken's Large Dictionary of Japanese (GLDJ), covering 100,000 words, definitions, examples, and grammatical explanations. The effectiveness of these techniques is currently being tested by creating a clustered semantic network for the GLDJ.As the applications of the graph clustering to the field of Humanities, we made the semantic networks from the lexical co-occurrence data of some historical documents or novels : the books of two contemporary thinkers, Cabanis and Mesmer, to measure the similarity of thinking between them ; the very famous novel of Saint-Exupery, "Le petit prince" to objectively propose a method of word sense disambiguation applicable to his enigmatic word usage. In this study we proposed as an alternative to the keyword-based clustering a new windowing method called Incrementally Advancing Window (TAW) that generates co-occurring word pairs that can be used as inputs to the Incremental Routing Algorithm. The results of the MCL applied to co-occurrence and/or adjacency data matrices were evaluated by using the indexes as weighted curvature, modularity Q and F measure.

我们开发了一种新的方法来解决使用MCL处理文档和语料库时观察到的聚类大小不平衡问题。分支MCL（BMCL）或潜在邻接矩阵可以将过度包含的马尔可夫聚类（核心聚类）调整为适当的子集。该方法被应用于从GLDJ（Gakken's Large Dictionary of Japanese）的大规模语料库构建的语义网络，该语料库覆盖10万个词、定义、示例和语法解释。作为图聚类在人文学科领域的应用，我们从一些历史文献或小说的词汇共现数据中构建语义网络：当代思想家Cabanis和Mesmer的著作，以衡量它们之间的思维相似性;圣·埃克萨勒伊的著名小说《小王子》，客观地提出了一种适用于他的神秘词用法的词义消歧方法。在这项研究中，我们提出了一种替代的基于关键字的聚类一个新的窗口方法称为增量推进窗口（TAW），生成共现的字对，可以用作输入的增量路由算法。利用加权曲率、模块性Q和F测度等指标对MCL应用于共生和/或邻接数据矩阵的结果进行了评价。

项目成果

テキスト分析における2部グラフクラスタリングの可能性

二分图聚类在文本分析中的潜力

• 发表时间: 2006
• 期刊: 情報処理学会研究報告 NL-174
• 作者: 赤間啓之;三宅真紀;鄭在玲
• 通讯作者: 鄭在玲

グラフクラスタリングを用いた文献解析の諸技法に関して-カバニスとメスメルのテキストを例に

关于使用图聚类进行文献分析的各种技术——以Cabanis和Mesmer的文本为例

• 发表时间: 2007
• 作者: Hiroyuki;Akama;赤間啓之
• 通讯作者: 赤間啓之

L'elaboration d'un reseau semantique par le raffinement du Markov Clustering -A partir des donnees lexicales du roman de Saint-Exupery, 《Lepetit prince》

马尔可夫聚类提纯语义的详细阐述 -圣埃克苏佩里罗马词典的一部分，《Lepetit Prince》

• 发表时间: 2008
• 期刊: Actes des 9es Journees internationales d'Analyse Statistique des Donnees Textuelles, Lyon, Presses Universitaires de Lyon 1
• 作者: Hiroyuki Akama;Maki Miyake;Jaeyoung Jung
• 通讯作者: Jaeyoung Jung

Development of a Web-based Composition Support System - Using Graph Clustering Methodologies Applied to an Associative Concepts Dictionary

基于网络的作文支持系统的开发 - 使用应用于关联概念词典的图聚类方法

• 发表时间: 2006
• 期刊: ICALT-2006
• 作者: Jaeyoung Jung;Maki Miyake;Nobuyasu Makoshi;Hiroyuki Akama
• 通讯作者: Hiroyuki Akama

How to Take Advantage of the Limitations with Markov Clustering?–The Foundations of Branching Markov Clustering (BMCL)

• 发表时间: 2008
• 期刊: The Mathematical Intelligencer
• 作者: Hiroyuki Akama;Maki Miyake;Jaeyoung Jung