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MeCab is an open-source text segmentation library for Japanese written text. It was originally developed by the Nara Institute of Science and Technology and is maintained by Taku Kudou (工藤拓) as part of his work on the Google Japanese Input project.
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
RDFLib is a Python library for working with RDF, [2] a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1).
A Dictionary-based Compressed Pattern Matching Algorithm (PDF), archived from the original (PDF) on March 13, 2003 "A unifying framework for compressed pattern matching". 1999: 89–96. CiteSeerX 10.1.1.50.1745. {{cite journal}}: Cite journal requires |journal= "Speeding Up String Pattern Matching by Text Compression: The Dawn of a New Era" (PDF).
PAWS is a WMF service that allows bot operators to execute Python code in a Jupyter Notebook setup. Pywikibot Pywikibot is a Python library for developing bot applications. It also contains a number of standard built-in scripts. It is arguably the most used bot framework. spectrum / threshold of usefulness
As 2025 approaches, Bitcoin (CRYPTO: BTC) finds itself navigating a shifting macroeconomic landscape, with fading tailwinds raising concerns about sustained momentum, according to a report. What ...
In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.