MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
Mallet ====== Website: MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. In addition to classification, MALLET includes tools for sequence tagging for applications such as
named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers. Topic models are useful for analyzing large collections of unlabeled text. The MALLET topic modeling toolkit contains efficient, sampling-based implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA. Many of the algorithms in MALLET depend on numerical optimization. MALLET includes an efficient implementation of Limited Memory BFGS, among
many other optimization methods. In addition to sophisticated Machine Learning applications, MALLET includes routines for transforming text documents into numerical representations that can then be processed efficiently. This process is implemented through a flexible system of "pipes", which handle distinct tasks such as tokenizing strings, removing stopwords, and converting sequences into count vectors. An add-on package to MALLET, called GRMM, contains support for inference in general graphical models, and training of CRFs with arbitrary graphical structure. Installation Prerequisites Java
17 or higher Maven 3. 6 or higher macOS Users The default Java installation on macOS is often outdated. We recommend installing OpenJDK via Homebrew: After installation, follow the instructions printed by Homebrew to add OpenJDK to your PATH, or add this to your shell configuration file (e. Building from Source To build MALLET from source, use Maven: This compiles the code, runs tests, and creates a JAR file in the directory. For details about the commands please visit the
API documentation and website at: List of Algorithms: Topic Modelling LDA Parallel LDA DMR LDA Hierarchical LDA Labeled LDA Polylingual Topic Model Hierarchical Pachinko Allocation Model (PAM) Weighted Topic Model LDA with integrated phrase discovery Word Embeddings (word2vec) using skip-gram with negative sampling Classification AdaBoost Bagging Winnow C45 Decision Tree Ensemble Trainer Maximum Entropy Classifier (Multinomial Logistic Regression) Naive Bayes Rank Maximum Entropy Classifier Posterior Regularization Auxiliary Model Clustering Greedy Agglomerative Hill Climbing K-Means K-Best Sequence Prediction
Models Conditional Random Fields Maximum Entropy Markov Models Hidden Markov Models Semi-Supervised Sequence Prediction Models Linear Regression
常见问题
记录了多少星标和分叉？
mimno/Mallet 有 1,031 个星标和 353 个分叉。
仓库排名是多少？
mimno/Mallet 排名第 11,982。
记录的语言和类别是什么？
mimno/Mallet 有 1,031 个星标，语言为 Java，类别为 Model Development。
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics.
In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers.
Topic models are useful for analyzing large collections of unlabeled text. The MALLET topic modeling toolkit contains efficient, sampling-based implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA.
Many of the algorithms in MALLET depend on numerical optimization. MALLET includes an efficient implementation of Limited Memory BFGS, among many other optimization methods.
In addition to sophisticated Machine Learning applications, MALLET includes routines for transforming text documents into numerical representations that can then be processed efficiently. This process is implemented through a flexible system of "pipes", which handle distinct tasks such as tokenizing strings, removing stopwords, and converting sequences into count vectors.
An add-on package to MALLET, called GRMM, contains support for inference in general graphical models, and training of CRFs with arbitrary graphical structure.
Installation
Prerequisites
Java 17 or higher
Maven 3.6 or higher
macOS Users
The default Java installation on macOS is often outdated. We recommend installing OpenJDK via Homebrew:
brew install openjdk maven
After installation, follow the instructions printed by Homebrew to add OpenJDK to your PATH, or add this to your shell configuration file (e.g., ~/.zshrc):
export PATH="/opt/homebrew/opt/openjdk/bin:$PATH"
Verify your Java version with java --version.
Building from Source
To build MALLET from source, use Maven:
mvn package
This compiles the code, runs tests, and creates a JAR file in the target/ directory.
Build Commands
Command
Description
mvn compile
Compile the source code
mvn test
Run all tests
mvn package
Build JAR file (includes tests)
mvn package -DskipTests
Build JAR file (skip tests)
mvn install
Install to local Maven repository
mvn javadoc:javadoc
Generate API documentation
Using as a Maven Dependency
Add MALLET to your project's pom.xml:
cc.mallet
mallet
2.1.1
Usage
After building with mvn package, you can use the command-line interface:
bin/mallet [command] --option value --option value ...
Type bin/mallet to get a list of commands, and use the option --help with any command to get a description of valid options.
