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1–24 of 52 · Most stars

TranslatedChinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, constituency parsing, semantic dependency parsing, semantic role labeling, coreference resolution, style transfer, semantic similarity, new word discovery, keyword and keyphrase extraction, automatic summarization, text classification and clustering, pinyin and Simplified/Traditional Chinese conversion, natural language processing

A very simple framework for state-of-the-art Natural Language Processing (NLP)

  • pytorch
  • nlp
  • named-entity-recognition
  • sequence-labeling

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

Java API for Natural Language Generation. Originally developed by Ehud Reiter at the University of Aberdeen’s Department of Computing Science and co-founder of Arria NLG. This git repo is the official SimpleNLG version.

  • simplenlg
  • natural-language-generation
  • nlg
  • realiser

Parse human names into title, given, middle, family, suffix, nickname and maiden name. Typed, zero-dependency, CJK-aware.

  • python
  • text-processing
  • text-parser
  • python-module

Translated斑海豹 (Spotted Seal) — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions.

  • bilingual
  • calibration
  • cpu-inference
  • decision-making

RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.

CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.