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Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. [2] It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers").
General Architecture for Text Engineering (GATE) is a Java suite of natural language processing (NLP) tools for man tasks, including information extraction in many languages. [1] It is now used worldwide by a wide community of scientists, companies, teachers and students. It was originally developed at the University of Sheffield beginning in 1995.
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.
A rule-based program, performing lexical tokenization, is called tokenizer, [1] or scanner, although scanner is also a term for the first stage of a lexer. A lexer forms the first phase of a compiler frontend in processing. Analysis generally occurs in one pass.
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.
Java Apache java.util.regex Java's User manual: Java GNU GPLv2 with Classpath exception jEdit: JRegex JRegex: Java BSD MATLAB: Regular Expressions: MATLAB Language: Proprietary Oniguruma: Kosako: C BSD Atom, Take Command Console, Tera Term, TextMate, Sublime Text, SubEthaEdit, EmEditor, jq, Ruby: Pattwo Stevesoft Java (compatible with Java 1.0 ...
Javadoc is a documentation generator created by Sun Microsystems for the Java language (now owned by Oracle Corporation) for generating API documentation in HTML format from Java source code. The HTML format is used for adding the convenience of being able to hyperlink related documents together.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.