Sudachi offers three tokenization modes (A, B, C) that control the granularity of segmentation, from shortest units to longest, allowing users to choose based on application needs like search or parsing.
Users can create and load custom dictionaries to handle domain-specific terminology, neologisms, or proper nouns, ensuring accurate tokenization for specialized texts.
Built in Java with optimized algorithms, Sudachi performs morphological analysis quickly, making it suitable for real-time applications and large-scale batch processing.
The analyzer supports plugins that can modify the tokenization process, such as normalizing text, extracting named entities, or applying custom rules during analysis.
Each token includes rich metadata like surface form, dictionary form, part-of-speech with fine-grained tags, reading in kana, and normalized form, providing detailed linguistic insights.
Search platforms use Sudachi to tokenize Japanese web pages and documents into meaningful terms for indexing. By selecting the appropriate tokenization mode, they balance recall and precision, improving search result relevance. This enables faster query matching and better handling of compound words, enhancing user experience in information retrieval systems.
Companies analyze Japanese social media posts and reviews using Sudachi to extract tokens for sentiment classification. The ability to handle informal language and neologisms via user dictionaries ensures accurate tokenization of slang and emojis. This leads to more reliable sentiment scores for brand monitoring and market research.
Data scientists preprocess Japanese text datasets for NLP models like BERT or LSTM using Sudachi. Tokenization into morphemes provides cleaner input features, improving model performance on tasks such as text classification or named entity recognition. The plugin architecture allows custom normalization steps tailored to specific datasets.
Researchers use Sudachi to analyze large Japanese text corpora, studying word frequency, part-of-speech distributions, and morphological patterns. The detailed token metadata supports linguistic studies on language evolution or dialect variations. Open-source nature allows customization for academic projects without licensing costs.
Media platforms employ Sudachi to tokenize article titles and descriptions in Japanese, enabling content tagging and similarity matching. Accurate segmentation helps in building user profiles based on interest keywords. This improves recommendation accuracy for news, videos, or products, driving engagement and retention.
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