A universal multilingual weightless neural network tagger via quantitative linguistics.

Area of Science:

• Computational Linguistics
• Natural Language Processing
• Machine Learning

Background:

• Multilingual part-of-speech tagging research has grown with increased language corpora availability.
• Existing mWANN-Tagger (multilingual weightless artificial neural network tagger) showed robustness but required language-specific parameter tuning.
• A universal tagger usable across languages without tuning is needed for true multilingual capability.

Purpose of the Study:

• To investigate the relationship between a language's lexical diversity and optimal mWANN-Tagger parameter configuration.
• To develop a universal parameter configuration for mWANN-Tagger applicable across diverse languages.
• To establish mWANN-Tagger as a truly universal multilingual part-of-speech tagger.

Main Methods:

• Corpus analysis across eight diverse languages to identify parameter tuning needs.
• Development and testing of a universal parameter configuration for mWANN-Tagger.
• Evaluation of the universal mWANN-Tagger on new corpora, including isolating to polysynthetic languages.
• Further experiments using Universal Dependencies treebanks.

Main Results:

• Preliminary analyses suggested a single parameter configuration could be effective for multiple languages.
• The universal parameter configuration yielded mWANN-Tagger instances as accurate as tuned, language-specific ones.
• The universal configuration proved effective across a wide range of languages, from isolating to polysynthetic.
• mWANN-Tagger demonstrated potential for extension and outperforming state-of-the-art taggers with improved word representations.

Conclusions:

• mWANN-Tagger can be applied to new corpora without parameter tuning, functioning as a universal multilingual part-of-speech tagger.
• The universal configuration eliminates the need for language-specific adjustments, enhancing usability.
• Further research into word representations can significantly improve mWANN-Tagger's performance and competitiveness.