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MeTA Review

2

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Excellent

Revainrating 5 out of 5  
Rating 
5.0
Artificial Intelligence, Conversational Intelligence

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Description of MeTA

MeTA is a modern C++ data sciences toolkit that allow text tokenization, including deep semantic features like parse trees, inverted and forward indexes with compression and various caching strategies, a collection of ranking functions for searching the indexes, topic models, classification algorithms, graph algorithms, language models, CRF implementation (POS-tagging, shallow parsing), wrappers for liblinear and libsvm (including libsvm dataset parsers), UTF8 support for analysis on various languages and .multithreaded algorithms

Reviews

Global ratings 2
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Type of review

Revainrating 5 out of 5

Excellent open-source tool and best solution provider

I have used this library in my research work to solve different problems related to NLP domain such as Text Classification using SVM classifier or Naive Bayes Classifiers etc., The source code was easy enought read even by beginners which helped me understand it easily without any problem at all. It also provides good documentation so one can figure out how does each function works from there itself rather than having some other resources available online about these libraries/frameworks. There

Pros
  • Great performance : Its performs much better compared with traditional way
  • Easy installation process : Very simple, user friendly framework that takes care off compiling our codes too.
  • Source Code is great
  • Documentation provided
  • Good amount enough
Cons
  • Some difficulties

Revainrating 5 out of 5

Robust toolkit designed by domain experts

The C++ API is very well documented and easy to use even if you are new to the library. I am working on implementing word embeddings using MeTA. Very good documentation is available online which makes it easy to implement an algorithm from scratch. I have not used any other libraries for this purpose so cannot compare with others. But I think MeTA has all the features required to build a robust system. If I were building such a system, MeTA would be my choice. Word embedding using MeTA will…

Pros
  • Documentation
  • Easy integration into existing code base.
  • Support forum (ask questions)
  • Available sample projects(word2vec) as examples when integrating custom classifiers/filters etc
Cons
  • I don't really have any dislikes about this, everything is just fine