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MeCab Morphological Analysis Engine

repository·master·Indexed 22 days ago

https://github.com/taku910/mecab

A morphological analysis engine featuring a CLI for part-of-speech analysis and language bindings for Perl, Python, Ruby, and Java. The engine includes tools for dictionary compilation, feature index management via EncoderFeatureIndex and DecoderFeatureIndex, and machine learning training through EncoderLearnerTagger and DecoderLearnerTagger. It provides C++ classes such as MeCab::Dictionary for dictionary operations and CharProperty for character metadata management.

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What's inside MeCab

  1. Install the MeCab Python module

    master

    To install the MeCab Python module, you must first build the extension and then install it using root/superuser privileges. You can optionally specify a custom installation directory using the --prefix option.

    # Build the module
python setup.py build

# Install the module (requires superuser privileges)
su
python setup.py install

# Alternatively, install to a specific directory
python setup.py install --prefix=/tmp/pybuild/foobar

  2. Install the MeCab Ruby module

    master

    To install the MeCab Ruby module, you must first generate the extension configuration, compile the source, and then install it with administrative privileges. Run the following commands from the module directory:

    ruby extconf.rb
make
su
# make install

  6. Run the MeCab Java sample program

    master

    To run the provided sample program (test.java), you must include MeCab.jar in your classpath and specify the path to your dictionary using the -d flag. Ensure the dictionary path is correctly relative to your execution context.

    java -classpath MeCab.jar test -d ../dic

  8. Use DecoderLearnerTagger for parsing

    master

    The DecoderLearnerTagger class is designed for the decoding/parsing phase of machine learning-based tagging. It encapsulates the necessary tokenizer and feature index data to process text.

    Key methods:

    • open(const Param &): Opens the tagger using a Param configuration object.
    • parse(std::istream *, std::ostream *): Performs the actual parsing of text from an input stream and writes the results to an output stream.
    DecoderLearnerTagger tagger;
tagger.open(params);
tagger.parse(&input_stream, &output_stream);

  9. Use POSIDGenerator to map keys to IDs

    master

    The POSIDGenerator class is used to map string-based keys (likely part-of-speech identifiers) to integer IDs based on rules loaded from a file. This is typically used to convert human-readable tags into a numeric format used by the analyzer.

    MeCab::POSIDGenerator generator;
if (generator.open("pos_rules.txt")) {
    int id = generator.id("NOUN");
    // id contains the mapped integer for the key "NOUN"
}

  10. Use EncoderFeatureIndex to build and save feature indices

    master

    The EncoderFeatureIndex class is used during the dictionary building/learning process to encode features and save them to disk.

    Key operations:

    • open(const Param &param): Initializes the encoder.
    • buildFeature(LearnerPath *path): Builds features for a given learner path.
    • save(const char *filename, const char *header): Saves the encoded index to a file with a specified header.
    • reopen(const char *filename, const char *charset, std::vector<double> *alpha, Param *param): Reopens an existing index with specific charset and alpha settings.
    • shrink(size_t freq, std::vector<double> *observed): Reduces the index size based on frequency and observed values.
    • clearcache(): Clears the internal feature cache.
    // Example usage of EncoderFeatureIndex methods
EncoderFeatureIndex encoder;
encoder.open(param);
encoder.buildFeature(path);
encoder.save("model.bin", "header_data");

  11. Manage MeCab dictionaries with the Dictionary class

    master

    The MeCab::Dictionary class provides an interface for opening, searching, and inspecting MeCab dictionary files. You can open a dictionary file in read mode (default) or other modes, and perform exact match or common prefix searches to retrieve tokens and their features.

    Key Operations

    • Open/Close: Use open(filename, mode) to load a dictionary and close() to release it.
    • Searching:
      • exactMatchSearch(key): Finds an exact match for the given key.
      • commonPrefixSearch(key, len, result, rlen): Finds the longest common prefix.
    • Token Retrieval: Once a search result (result_type) is obtained, use token(result) to get the Token structure and feature(token) to access the feature string.
    MeCab::Dictionary dic;
if (dic.open("mecab-ipadic-utf8.dic", "r")) {
    auto result = dic.exactMatchSearch("search_key");
    if (result.value != 0) {
        const MeCab::Token* t = dic.token(result);
        const char* feat = dic.feature(*t);
    }
    dic.close();
}

  12. Initialize and manage a MeCab Tokenizer

    master

    The MeCab::Tokenizer class is the primary interface for performing morphological analysis. To use it, you must first instantiate the Tokenizer and then call open(const Param &param) to load the necessary dictionaries and configuration parameters. Once analysis is complete, call close() to release resources. The Tokenizer lifecycle is managed via its constructor and destructor, which automatically calls close().

    MeCab::Tokenizer tokenizer;
// param must be configured with dictionary paths and settings
if (tokenizer.open(param)) {
    // Perform analysis using tokenizer.lookup(...)
    tokenizer.close();
}