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

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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.

Diacritisation

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


Arabic:

Lemmatisation

When you search a word in inflected form, your dictionary program can show you the result, during which the lemmatisation is done.


Multiple languages:

Pitch-Accent Marking

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


Japanese:

Stress Generation

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


Russian:

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:

Japanese:

Lao:

Thai:

Vietnamese:

Contributors

GrimPixel and Maintenance script


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