搜索资源列表
准确率召回率及f值的计算源码
- 准确率召回率及f值的计算源码，本代码主要用于上述三个数值的计算，主要针对自然语言处理领域检索结果。-the values of the F and the values of Recall and the precession 。the program is offen used in the area of NLP .
KNN
- 这是一个用于自然语方处理的分类算法（KNN算法）人源代码-the source code of KNN algorithm in NLP
FudanNLP-1.05
- fudanNLP自然语言处理1.5开源包，用于自动分类，文档摘要，语义分析等-fudan NLP
NLP
- 中文自然语言处理 人物职称识别 根据统计学方法来识别 通过语法 寓意 语用来加以处理- Through the studies on the knowledge of modern Chinese such as grammar,syntax,semante- me,pragmatic,the internal regularity is discovered and the collections are considered to be the organic compositions
CRFPP-0.58
- CRF ++是一个简单的，可定制的，条件随机域（控释肥）分割/标记顺序数据的开源实现。 CRF ++是专为通用的目的，将被应用到各种各样的NLP任务，如命名实体识别，信息提取和文本组块。-CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is design
NLP
- 中文文本情感分类。上课的课程作业，本着服务大家的思想上传，包括实验代码，实验数据，实验ppt以及实验报告。非常详细-Chinese text sentiment classification. Course work in class, in the service of everyone' s thoughts upload, including experimental code, test data, test and lab reports ppt. Very detailed.
CA
- 改进文化差分算法，用于LP，NLP,MILP,MINLP数学模型的求解-Improved cultural difference algorithm for solving LP, NLP, MILP, MINLP mathematical models
pyltp-master
- 自然语言处理包，来源于哈尔滨工业大学ir实验室 python接口-NLP FROM HIT
NLP-speech-tagging
- 基于隐马尔可夫模型的中文分词、词性标注、命名实体识别-Based on Chinese word hidden Markov model, speech tagging, named entity recognition
NLP
- Tensorflow框架下的手写数字识别-In recognition of handwritten numerals frame Tensorflow
CRF++-0.58
- CRF经典demo，一般用于NLP（自然语言处理）(CRF---NLP---a good demo)
main
- NLP-NER的一个简单Demo。模型基于LSTM-CRF(A Demon for Chinese-NER from https://github.com/zjy-ucas/ChineseNER)
seq2seq
- python基于tflearn实现nlp中seq2seq算法(this code implements the seq2seq algorithm using python and tflearn frame.)
DI
- 使用爬虫爬取医疗对话文本信息，并使用tensorflow进行NLP分析和分类。(obtain data using crow programme and then use TensorFlow to realize an NLP system to classify the data.)
Sentiment Analysis1
- 读取csv或xlsx文件后对文本信息进行NLP情感分析并计算出情感的得分，越接近1越积极，越接近-1越消极(After reading the CSV or xlsx file, NLP sentiment analysis is performed on the text information and the score of sentiment is calculated. The closer to 1, the more positive, the more close to - 1,