Language/Multiple-languages/Culture/Text-Processing-Tools

In this lesson, several useful linguistic tools useful for common language learners are discussed. They are not always accurate, so keep in mind.

Many of the tools introduced are written in Python, which is an important language in machine learning and easy to learn.

If you don't know Python, please try this:

In progress.

Diacritization

In Arabic writing system, diacritics indicate the accents, but they are often omitted for writing fluently. The process of restoring diacritics is called diacritization.

Arabic:

• Arabycia https://github.com/mohabmes/Arabycia
• Farasa https://github.com/MagedSaeed/farasapy
• Mishkal https://sourceforge.net/projects/mishkal/
• Pipeline-diacritizer https://github.com/Hamza5/Pipeline-diacritizer
• Shakkala https://github.com/Barqawiz/Shakkala
• Shakkelha https://github.com/AliOsm/shakkelha

Pitch-Accent Marking

In Japanese and other languages, the pitch-accent is important on distinguishing different words. They are unwritten and required.

Japanese:

• Prosody Tutor Suzuki-kun http://www.gavo.t.u-tokyo.ac.jp/ojad/phrasing/index
• tdmelodic https://github.com/PKSHATechnology-Research/tdmelodic

Stress Generation

In Russian and other languages, the stress is important on distinguishing different words. They are usually omitted.

Russian:

• RussianGram https://russiangram.com/
• Russian Stress Finder https://www.readyrussian.org/WebApps/StressFinder/

Word Segmentation

In some languages, words are not separated by spaces, for example: Chinese, Japanese, Lao, Thai. In Vietnamese, spaces are used to divide syllables instead of words. This brings about difficulties for computer programs like VocabHunter, gritz and text-memorize, where words are detected only with spaces.

The solution is called “word segmentation”, which detects words and insert spaces in between or put the segmented words into a list. You may want to ask: The programs only recognise spaces as the word separators, how to deal with Vietnamese? The answer is using the non-breaking space.

Chinese:

• Ansj https://github.com/NLPchina/ansj_seg
• CoreNLP https://github.com/stanfordnlp/CoreNLP
• FoolNLTK https://github.com/rockyzhengwu/FoolNLTK
• FudanNLP https://github.com/FudanNLP/fnlp
• HanLP https://github.com/hankcs/HanLP
• jieba https://github.com/fxsjy/jieba
• LAC https://github.com/baidu/lac
• LTP https://github.com/HIT-SCIR/ltp
• SnowNLP https://github.com/isnowfy/snownlp
• pkuseg https://github.com/lancopku/pkuseg-python
• pyhanlp https://github.com/hankcs/pyhanlp
• THULAC https://github.com/thunlp/THULAC-Python

Japanese:

• janome https://github.com/mocobeta/janome
• Juman++ https://github.com/ku-nlp/jumanpp
• Kagome https://github.com/ikawaha/kagome
• Kuromoji https://github.com/atilika/kuromoji / https://github.com/takuyaa/kuromoji.js/
• KyTea http://www.phontron.com/kytea/
• MeCab https://taku910.github.io/mecab/
• nagisa https://github.com/taishi-i/nagisa
• Sudachi https://github.com/WorksApplications/Sudachi / https://github.com/WorksApplications/SudachiPy

Lao:

• Lao Word-Segmentation https://github.com/frankxayachack/LaoWordSegmentation

Thai:

• Cutkum https://github.com/pucktada/cutkum
• CutThai https://github.com/pureexe/cutthai
• Deepcut https://github.com/rkcosmos/deepcut
• PyThaiNLP https://github.com/PyThaiNLP/pythainlp
• SWATH https://www.cs.cmu.edu/~paisarn/software.html
• SynThai https://github.com/KrakenAI/SynThai
• TLTK https://pypi.org/project/tltk/
• wordcut https://github.com/veer66/wordcut / https://github.com/veer66/wordcutpy

Vietnamese:

• DongDu https://github.com/rockkhuya/DongDu
• JVnSegmenter http://jvnsegmenter.sourceforge.net/
• Roy_VnTokenizer https://github.com/roy-a/Roy_VnTokenizer
• VietSeg https://github.com/manhtai/vietseg
• VnCoreNLP https://github.com/vncorenlp/VnCoreNLP