Semilar

DM & LA Portal > Form: Data mining and learning analytics tools

SEMILAR alpha

Developed by: Vasile Rus et al.
License: Academic Free License (AFL)
Web page : Tool homepage
Tool type : Application software

The last edition of this page was on: 2014/03/19

The Completion level of this page is : Low

The last edition of this page was on: 2014/03/19 The Completion level of this page is : Low

SHORT DESCRIPTION

The goal of the SEMantic simILARity software toolkit (SEMILAR; pronounced the same way as the word 'similar') is to promote productive, fair, and rigorous research advancements in the area of semantic similarity. The kit is available as application software or as Java API.

As of March 2014, the GUI-based SEMILAR application is only available to a limited number of users who commit to help improving the usability of the interface. The JAVA libray (API) however, can be downloaded.

SEMILAR comes with various similarity methods based on Wordnet, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), BLEU, Meteor, Pointwise Mutual Information (PMI), Dependency based methods, optimized methods based on Quadratic Assignment, etc. And the similarity methods work in different granularities - word to word, sentence to sentence, or bigger texts. Some methods have their own variations which coupled with parameter settings and your selection of preprocessing steps could result in a huge space of possible instances of the same basic method.

TOOL CHARACTERISTICS

Usability

Authors of this page consider that this tool is rather easy to use.

Tool orientation

This tool is designed for general purpose analysis.

Data mining type

This tool is made for Text mining.

Manipulation type

This tool is designed for Data analysis.

* * *

IMPORT FORMAT :

EXPORT FORMAT :

Tool objective(s) in the field of Learning Sciences

☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware

☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation

Tool can perform:

* Data extraction of type:

* Transformation of type:

* Data analysis of type: Data mining methods and algorithms

* Data visualisation of type: (These visualisations can be interactive and updated in "real time")

ABOUT USERS

Tool is suitable for:

Students/Learners/Consumers

Teachers/Tutors/Managers

Researchers

Developers/Designers

Organisations/Institutions/Firms

Others

Required skills:

STATISTICS:

PROGRAMMING:

SYSTEM ADMINISTRATION:

DATA MINING MODELS:  Basic

FREE TEXT

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Tool version : SEMILAR alpha
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Developed by : Vasile Rus et al.
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Tool Web page : http://www.semanticsimilarity.org/
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Tool type : Application software
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License:Academic Free License (AFL)

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## Contents

* 1 SHORT DESCRIPTION
* 2 TOOL CHARACTERISTICS
* 3 ABOUT USER
* 4 OTHER TOOL INFORMATION

## SHORT DESCRIPTION[edit source]

* * *

The goal of the SEMantic simILARity software toolkit (SEMILAR; pronounced the same way as the word 'similar') is to promote productive, fair, and rigorous research advancements in the area of semantic similarity. The kit is available as application software or as Java API.

As of March 2014, the GUI-based SEMILAR application is only available to a limited number of users who commit to help improving the usability of the interface. The JAVA libray (API) however, can be downloaded.

SEMILAR comes with various similarity methods based on Wordnet, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), BLEU, Meteor, Pointwise Mutual Information (PMI), Dependency based methods, optimized methods based on Quadratic Assignment, etc. And the similarity methods work in different granularities - word to word, sentence to sentence, or bigger texts. Some methods have their own variations which coupled with parameter settings and your selection of preprocessing steps could result in a huge space of possible instances of the same basic method.

## TOOL CHARACTERISTICS[edit source]

* * *

Tool orientation | Data mining type | Usability

|
|

This tool is designed for general purpose analysis. | This tool is designed for Text mining. | Authors of this page consider that this tool is rather easy to use.
Data import format | Data export format

|

. | .

Tool objective(s) in the field of Learning Sciences

☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware

☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation

Can perform data extraction of type:

Can perform data transformation of type:

Can perform data analysis of type:
Data mining methods and algorithms

Can perform data visualisation of type:
(These visualisations can be interactive and updated in "real time")

## ABOUT USER[edit source]

* * *

Tool is suitable for:

Students/Learners/Consumers:☑ | Teachers/Tutors/Managers:☑ | Researchers:☑ | Organisations/Institutions/Firms:☑ | Others:☑
Required skills:

Statistics:  | Programming:  | System administration:  | Data mining models: BASIC

## OTHER TOOL INFORMATION[edit source]

* * *

SEMILAR
Academic Free License (AFL)
Free&Closed source
Vasile Rus et al.
alpha
http://www.semanticsimilarity.org/
The goal of the SEMantic simILARity software toolkit (SEMILAR; pronounced the same way as the word 'similar') is to promote productive, fair, and rigorous research advancements in the area of semantic similarity. The kit is available as application software or as Java API.

As of March 2014, the GUI-based SEMILAR application is only available to a limited number of users who commit to help improving the usability of the interface. The JAVA libray (API) however, can be downloaded.

SEMILAR comes with various similarity methods based on Wordnet, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), BLEU, Meteor, Pointwise Mutual Information (PMI), Dependency based methods, optimized methods based on Quadratic Assignment, etc. And the similarity methods work in different granularities - word to word, sentence to sentence, or bigger texts. Some methods have their own variations which coupled with parameter settings and your selection of preprocessing steps could result in a huge space of possible instances of the same basic method.

General analysis
Researchers
Basic
Application software
Text mining
Data analysis
Data mining methods and algorithms
rather easy to use
Low

Bibliography

See SEMILAR: A Semantic Similarity Toolkit, for a complete list.

* Rus, V., Lintean, M., Banjade, R., Niraula, N., and Stefanescu, D. (2013). SEMILAR: The Semantic Similarity Toolkit. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, August 4-9, 2013, Sofia, Bulgaria. PDF Reprint