A Go library for efficient multilingual text segmentation and NLP, supporting English, Chinese, Japanese, and more.
Gse is a Go library for efficient multilingual text segmentation and natural language processing. It splits text into words or tokens across languages like English, Chinese, and Japanese, solving problems in search indexing, text analysis, and language-specific tokenization.
Developers building search engines, text analysis tools, or multilingual applications in Go who need fast and accurate text segmentation.
It offers high-performance segmentation with multiple algorithms, broad language support, and seamless integration with tools like Elasticsearch, all in a native Go package.
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others.
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Achieves concurrent speeds up to 26.8MB/s using double array trie and shortest path algorithms, as benchmarked in the tools/benchmark directory, making it ideal for high-throughput applications.
Supports English, Chinese (Simplified and Traditional), Japanese, and others with dedicated dictionary loading, enabling robust text processing across diverse languages without external dependencies.
Offers common, search engine, full, precise, and HMM modes, providing adaptability for different use cases like search indexing or precise text analysis, as highlighted in the feature list.
Compatible with Elasticsearch and Bleve, and includes JSON RPC service support, allowing easy integration into existing search pipelines and distributed systems.
Advanced capabilities like NLP by TensorFlow and Named Entity Recognition are listed as 'in work' in the README, limiting its utility for comprehensive NLP tasks beyond segmentation.
Requires explicit loading and configuration of user and embed dictionaries, which can be cumbersome for quick setups or those unfamiliar with Go's embedding features, as seen in the example code.
While it has bindings for JavaScript, the overall ecosystem is smaller compared to Python NLP libraries, and documentation, though multilingual, may lack depth for advanced use cases.