應用區域敏感雜湊對文獻進行分類之研究

李治平

184.72.102.217，Hello！

Close
Search Quick Search Advanced Search Publication Search	Personal Service Member Login Searching History Use Bonus Card SYMSKAN
Browse Browse by University of Graduate Thesis Browse by Pinyin of Graduate Thesis Browse by Conference Proceedings Topic Browse by Conference Proceedings Publisher Browse by Pinyin of Conference Proceedings Browse by eBook Category Browse by eBook Topic Browse by eBook Publisher
Customer Services Authorize Airiti Recommend a Journal

Search Symbol (Half-width)	Description of Search Symbols
Space	"AND" indicates the intertwining of key terms used in a search
Double Quotation Marks ("") ( " " )	Double quotation marks indicate the beginning and end of a phrase, and the search will only include terms that appear in the same order of those within the quotations. Example: "image process" ： " image process "
?	Indicates a variable letter. Entering two ? will indicate two variable letters, and so on. Example: "Appl?", search results will yield apple, apply… e , appl y … ( (often used to English word searches) )
*	Indicates an unlimited number of variable letters to follow, from 1~n. Example: Enter "appl*", search results will yield apple, apples, apply, applied, application…(often used in English word searches) e , appl es , appl y , appl ied , appl ication … ( (often used to English word searches) )
AND、OR、NOT	Boolean logic combinations of key words is a skill used to expand or refine search parameters. (1) AND (1) AND: Refines search parameters (2) OR (2) OR: Expands search parameters (3) NOT: Excludes irrelevant parameters

DOI stands for Digital Object Identifier （ D igital O bject I dentifier ） ,
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.

Using DOI as a persistent link

To create a persistent link, add「http://dx.doi.org/」「 http://dx.doi.org/ 」 before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.

Cite a document with DOI

When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.

DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ）。

Data Source

College of Business > Graduate School of Information Management
Social Sciences > Management

Bibliography Management Tool

Related Links

Report an Issue

Search in Library

There is no full-text available at the moment. Airiti would like to request author's authorization. I would like to provide authorization.

應用區域敏感雜湊對文獻進行分類之研究

A Language Neutral Text Classification Method Using Locality Sensitive Hashing

李治平 , Masters　　Advisor：陳宗天　　

繁體中文

空間向量模型；區域敏感雜湊；文獻分類； Document Classification ； Locality Sensitive Hashing ； Vector Space Model

Abstract │ Reference (47) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Most of the contemporary text clustering or classification methods are based on texting mining technique that characterize documents using features derived from the frequency of term’s occurrence and the distribution of these terms in the document corpus. In these methods, a document is represented by a vector of terms’ frequency and their inverse document frequency (TF-IDF). The similarity between documents is measured by the cosine of their vector representation. However, the TF/IDF based methods were devised to process English language. Other languages may have a very different form that may render the term-based method inapplicable. We propose a language neutral classification/clustering method that utilizes the Locality Sensitive Hashing (LSH) that does not rely on lexical terms in a document. We compare the accuracy between the classical Vector Space Model (VSM) and LSH method and found their performance on par. Our proposed method is indeed a language neutral text classification/clustering scheme that is suitable for very large document corpus.

Reference ( 47 ) 〈TOP〉

Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175-185.
連結：
Aslam, J. A., & Pavlu, V. (2007). Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions. Paper presented at the European Conference on Information Retrieval.
連結：
Baoli, L., Qin, L., & Shiwen, Y. (2004). An adaptive k-nearest neighbor text categorization strategy. ACM Transactions on Asian Language Information Processing (TALIP), 3(4), 215-226.
連結：
Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404.
連結：
Broder, A. Z. (1997, June). On the resemblance and containment of documents. In Compression and Complexity of Sequences 1997. Proceedings (pp. 21-29). IEEE.
連結：

Altmetrics

〈TOP〉

E-mail ：

When an article is available to download, a notice will be sent to your mailbox address.

E-mail ：