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ansj_seg is a Java NLP toolkit and segmentation library designed for processing Chinese text. It functions as a word segmenter, part-of-speech tagger, and named entity recognizer to divide continuous Chinese characters into meaningful words and tokens.
The main features of nlpchina/ansj_seg are: Chinese Language Segmenters, Chinese NLP Libraries, Chinese Entity Recognizers, Named Entity Recognition, NLP Toolkits, Part-of-Speech Taggers, Part-of-Speech Tagging Pipelines, Generative Word Segmenters.
Projects with overlapping indexed features include: isnowfy/snownlp — SnowNLP is a Python library for Chinese natural language processing. It provides tools for text segmentation,… lancopku/pkuseg-python — pkuseg-python is a Chinese word segmentation toolkit and natural language processing library. It provides specialized… baidu/lac — LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and… fxsjy/jieba — This project is a Chinese text segmentation library and tokenizer designed to split Chinese sentences into individual… hankcs/hanlp — HanLP is a natural language processing library and deep learning framework specifically optimized for the Chinese… ownthink/knowledgegraphdata — KnowledgeGraphData is a collection of structured datasets and corpora designed to provide a foundational layer for…
SnowNLP is a Python library for Chinese natural language processing. It provides tools for text segmentation, sentiment analysis, document classification, and phonetic transliteration. The library includes capabilities for training and saving custom machine learning models for tokenization and sentiment analysis using raw training datasets. It covers a range of linguistic processing areas, including parts of speech tagging, sentence splitting, and text similarity measurement. The toolkit also provides utilities for extracting key information through text summarization and calculating word im
pkuseg-python is a Chinese word segmentation toolkit and natural language processing library. It provides specialized models for splitting Chinese text into words across various domains, including news, medical, and web content, and includes a tool for assigning grammatical parts of speech tags to segmented words. The library allows for the training of custom segmentation models using annotated datasets and supports the integration of user-defined dictionaries to ensure specialized terminology is recognized correctly. It employs a multi-threaded execution engine to process large volumes of Ch
LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and named entity recognition. It functions as a high-performance system that identifies word boundaries and grammatical categories using trained machine learning models. The project features a lightweight, compiled native runtime that enables on-device natural language processing and embedding into mobile applications. It includes model compression and conversion to optimize for resource-constrained environments and supports multi-threaded parallel execution to increase throughpu
This project is a Chinese text segmentation library and tokenizer designed to split Chinese sentences into individual words. It serves as a natural language processing tool for splitting characters into words, tagging parts of speech, and extracting keywords using statistical analysis. The library distinguishes itself through support for custom dictionary configuration and vocabulary file management, allowing users to override default segmentation rules for domain-specific accuracy. It also includes a TF-IDF keyword extractor to identify significant words and core topics within documents. Th