Kylm is a Java-based application designed to help you compare the effectiveness of different language models. The tool provides support for multiple smoothing methods and can be used in command line mode.
This language modelling toolkit provides the option to model unknown words by using sub-word units (characters).
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Kylm is designed to analyze either a single text or multiple documents, comparing the effectiveness of language models for each. By using smoothing methods and running tests on different language models and different units (sub-word units), the tool can be used to assess the strengths and weaknesses of each language model.
* Smoothing methods implemented
* Adjustable time interval for analysis
* Compression of text and models
* Iterations for required accuracy
* Comparing models by their raw performance
* Formal documentation
* Command line interface
* Option to compare smoothed models
* Option to generate PDF reports
* Display of the best performing model
* Customized report generation
* Option to append the results to an existing document
1. Download and extract all required files. [Installation Guide]
2. Start Kylm and click on add documents.
3. Add files and open the sequence.
4. Click on the window to the right of the analysis window.
5. Click on the “Smooth Word” tab on the left and select the smoothing method you want to use.
6. In the text area, please enter the text in the format “String_section_int_doc”
7. In the output area, select the desired output format (PDF, Word, XML).
8. Click on “Run Smoothing”, and optionally start the analysis.
9. Once completed click on “View Results” and select how many iterations you want to perform for the calculation.
* Email support is available from:
Kylm Bug Tracker:
To report bugs you can do so via email to Kylm_Support at gmail.com
* If you are a developer you can also log bugs for the Kylm library here:
* If you found a bug or have some enhancement requests, please submit the bug report and/or the enhancement request to Kylm_Support at gmail.com
We’ve just rolled out an update to the Kylm language model comparison tool. This update makes it easier to run multiple language model comparisons within a single run. It also allows you to specify the analysis intervals for the Smoothing methods.
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Kylm tool is a free language modelling application developed using NetBeans IDE. It can provide useful information about the language model creation process as well as offer an alternative to commercial products available for this purpose. With Kylm tool, you can create a language model and choose from several smoothing methods. The service can be accessed through a web-based user interface, and it can be also used in command line mode.
Language Model to Text (LM2T) is a free Java software package that can be used to determine the most likely sequence of words that gave rise to a given text. The package is based on acoustic models of words and phrases computed from a large, standard language model corpus and a language model trained using a sample set of the text. LM2T is free software distributed under a 3-clause BSD license.
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Stochastic Context Free Grammar (SCFG) Generator is a free software for generating statistical context-free grammars from strings generated by sample text generation systems. These grammars can then be used as an input for statistical language models that are suited to the application in which they are going to be used.
Generic Constraint Grammar (GCG) Generator is a set of tools that can generate a collection of grammars from a corpus of sentences. These grammars can then be combined into context-free grammars using generative stochastic context-free grammars.Factors associated with life satisfaction in anorexia nervosa.
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The purpose of this research was to evaluate three different
methods for language model smoothing using a well-known n-gram
language model. These methods are simple regression, iterative
smoothing, and truncated Viterbi smoothing. When compared with the
maximum likelihood solution, these smoothing methods result in
higher performance on a variety of statistical tasks.
In the paper, they claim that
On average, iterative smoothing achieved on-par or better performance
than maximum likelihood, for all tasks. Furthermore, it outperformed
However, they do not show the plots of the comparisons. Where can I see the plots?
PS: This is related to this question.
The paper is available for download here.
The authors explicitly mention that the plots are available from
their software page. See
The authors provide software code to reproduce their experiments. In
you can find the baseline code for the experiments.
Also, for your last query, in the paper they are using SVM’s classifier, the code is available in the github, the link is
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The primary goal of the examination of the physical demands of various surgical specialties is to provide guidance in the training and selection of personnel for specific operative procedures. Basic skills, such as doing vertical/horizontal and horizontal/vertical shoulder throws, steady and rapid hand/arm movements, and grip strength, have been used to evaluate athletes for participation in sporting events such as tennis, golf, baseball, and football. During the last 2 decades, similar tests have been developed to evaluate surgeons and have become a standard test battery in use in many institutions. This article reviews the development and use of surgical skills assessments.Eugenio Ghella
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Ghella began his career as a teacher in
What’s New In?
1. Support multiple smoothing methods for sequence.
2. Command line mode with more than one options.
3. Model unknown words as sub-words.
4. Split your sentence in train and test sets.
5. Synthesize a test set automatically.
6. Accuracy reports based on your model and your test set.
7. Comparing two different models on the same test set and keep the one that
8. Optionally save the model in a package.
9. A collection of built in test cases for you to test your application.
10. Languages and tools support.
11. Perfect for beginners and casual users.
12. Many language models are supported, including Google’s FastText
13. Supporting CKJ (Chinese-Japanese language pair).
14. Supporting Chinese and Spanish.
15. Perfect for translators and for people who provide training data.
16. If you do not want to give your source code to the tool, we also provide
a test model.
Kylm includes support for the following languages:
– English (source:
– Chinese (source:
– Spanish (source:
– Japanese (source:
– German (source:
Known languages are not supported:
– Korean (source:
Tested languages are not included:
– Punjabi (source:
– Tibetan (source:
– The prediction for unknown words may not be perfect. If you are only
interested in the accuracy of the unknown words, please ignore the unknown
– We do not support unknown words in one-to-one language pairs. For example, if
you are using
System Requirements For Kylm:
1. 600 MHz, or faster, Intel or AMD processor
2. 4 GB of RAM, or more if you plan to use MIRIUSA with Windowed mode
3. 128 MB of video memory
4. A “WII” controller or equivalent device (Nunchuk)
5. DirectX 9 hardware-accelerated graphics card
6. Windows 7 (or later) or Mac OS X (or later)
7. DVD-drive or USB port
8. USB mouse
9. Mouse-controlled joystick