Automated Chinese Essay Scoring using Vector Space Models

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

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Vector Space Models for the Classification of Short Messages on Social Network Services

Lecture Notes in Business Information Processing - Web Information Systems and Technologies ◽

10.1007/978-3-662-44300-2_13 ◽

2014 ◽

pp. 209-224 ◽

Cited By ~ 2

Author(s):

Ricardo Lage ◽

Peter Dolog ◽

Martin Leginus

Keyword(s):

Social Network ◽

Vector Space ◽

Network Services ◽

Vector Space Models ◽

Social Network Services ◽

Short Messages

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Text Similarity in Vector Space Models: A Comparative Study

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) ◽

10.1109/icmla.2019.00120 ◽

2019 ◽

Cited By ~ 3

Author(s):

Omid Shahmirzadi ◽

Adam Lugowski ◽

Kenneth Younge

Keyword(s):

Comparative Study ◽

Vector Space ◽

Text Similarity ◽

Vector Space Models

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A Systematic Review on Author Identification Methods

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2017040106 ◽

2017 ◽

Vol 4 (2) ◽

pp. 81-91 ◽

Cited By ~ 2

Author(s):

Sunil Digamberrao Kale ◽

Rajesh Shardanand Prasad

Keyword(s):

Systematic Review ◽

Vector Space ◽

Probabilistic Models ◽

Learning Models ◽

Plagiarism Detection ◽

Identification Methods ◽

Hybrid Approaches ◽

Author Identification ◽

Meta Learning ◽

Vector Space Models

Author Identification is a technique for identifying author of anonymous text. It has near about 130 year's long history, started with the work by Mendenhall 1987. Applications of Author identification include plagiarism detection, detecting anonymous author, in forensics and so on. In this paper the authors outline features used for Author identification like vocabulary, syntactic and others. Researchers worked on various methods for Author identification they also outline this paper on types of Author Identification methods that include 1. Profile-based Approaches which includes Probabilistic Models, Compression Models, Common n-Grams (CNG) approach, 2. Instance-based Approaches which includes Vector Space Models, Similarity-based Models, Meta-learning Models and 3. Hybrid Approaches. At the end the authors conclude this paper with observations and future scope.

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