SemantiLib: An open-source Python library encapsulates the developed approaches and methods within a single unsupervised NLP pipeline, based on advanced high-performance computing (HPC) technologies.
An Open source library for Python has been developed that encapsulates the developed approaches and methods within a single Unsupervised Natural Language Processing Pipeline based on advanced high-performance computing technologies. The library encapsulates multiple unsupervised natural language processing techniques into a single Pipeline optimized using high-performance computing technologies. The main components of the conveyor have been developed. The developed library based on HPC technologies software implements the following algorithms: normalization and tokenization of large text corpora, an algorithm for identifying the target set of tokens from their complete set, methods for the generative formation of context sets, methods for distributive semantic analysis, various methods of minimum-sum-of-squares clustering optimized for processing large text data, etc. Most of the algorithms included in this library have both a multiprocessor implementation and a GPU-based implementation.
With the dual capability of multi-processor and GPU-based implementations, the library ensures flexibility and scalability in its deployment. Users with varying hardware configurations can efficiently utilize the library, be it on conventional multi-core CPU systems or on platforms equipped with advanced GPUs. This adaptability not only ensures that processing is optimized based on the available resources but also ensures that the library is future-proof, ready to harness advancements in computing hardware.
Another hallmark of this library is its intuitive API. Even though it encompasses a wide array of advanced algorithms and techniques, the interface remains user-friendly, allowing both novices and experts to get started with minimal ramp-up time. The modular architecture also means that users can plug in specific components as needed, ensuring that they can tailor the pipeline to their unique requirements.
Developed library makes efficient use of memory and computational resources, ensuring that large-scale datasets can be processed without unnecessary overheads. Sophisticated caching mechanisms, combined with advanced memory management techniques, guarantee swift data access times and efficient algorithm execution. Furthermore, with its open-source nature, the community can actively contribute, enhancing its features, optimizing existing algorithms.