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smilelight / lightNLP

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基于Pytorch和torchtext的自然语言处理深度学习框架，包含序列标注、文本分类、句子关系、文本生成、结构分析、五大功能模块，已实现了命名实体识别、中文分词、词性标注、语义角色标注、情感分析、关系抽取、语言模型、文本相似度、文本蕴含、依存句法分析、词向量训练、聊天机器人、机器翻译、文本摘要等功能。框架功能丰富，开箱可用，极易上手！基本都是学习他人实现然后自己修改融合到框架中，没有细致调参，且有不少Bug～

python nlp deep-learning text-classification word2vec pytorch chinese pos skip-gram cbow language-model cws dependency-parsing srl relation-extraction sentence-similarity hierarchical-softmax torchtext negative-sampling nature-language-process
  • Updated Jan 10, 2020
  • Python

yongzhuo / Macropodus

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自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构，中文分词，词性标注，命名实体识别，新词发现，关键词，文本摘要，文本相似度，科学计算器，中文数字阿拉伯数字(罗马数字)转换，中文繁简转换，拼音转换。tookit(tool) of NLP，CWS(chinese word segnment)，POS(Part-Of-Speech Tagging)，NER(name entity recognition)，Find(new words discovery)，Keyword(keyword extraction)，Summarize(text summarization)，Sim(text similarity)，Calculate(scientific calculator)，Chi2num(chinese number to arabic number)

nlp crf similarity text-summarization keyword ner albert cws newword calulator macropodus segnment
  • Updated May 14, 2020
  • Python
ArticutAPI

Droidtown / ArticutAPI

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API of Articut 中文斷詞 (兼具語意詞性標記)：「斷詞」又稱「分詞」，是中文資訊處理的基礎。Articut 不用機器學習，不需資料模型，只用現代白話中文語法規則，即能達到 SIGHAN 2005 F1-measure 91% 以上，Recall 96% 以上的成績。

nlp natural-language-processing nlu artificial-intelligence cws pos-tagging part-of-speech-tagger pos-tagger natural-language-understanding part-of-speech-embdding
  • Updated May 6, 2020
  • Python

liweimin1996 / Min_NLP_Practice

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Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。

nlp tensorflow crf keras cnn entity pos ner cws bilstm
  • Updated Aug 11, 2019
  • Python

BrandonEdwards / bbsBayes

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Open

Documentation for `fetch_bbs_data()`

TrashBirdEcology
TrashBirdEcology commented Apr 4, 2020

The documentation for fetch_bbs_data does not inform me where the data is saving on machine, nor does it allow me to specify a location. Without having looked into the function, I haven't a clue where or how the data is downloaded and saved(?) locally. Please edit the documentation to inform us.

Further, I cloned the package, ran r cmd check (devtools::load_all()), then ran `fetch_bbs_data()

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