使用lda、tf-idf等无监督算法加停用词表抽取专利主题信息构建企业间关系三元组
Extracting patent subject information using unsupervised algorithms such as lda and tf idf and stopping thesaurus (2023-11-02, Jupyter Notebook, 0KB, 下载0次)
分析并将BBC 2004-2005年的新闻文章分为五个主题类别：商业、娱乐、政治、体育和科技使用...，
Analyze and cluster BBC news articles from 2004-2005 into five topical categories: business, entertainment, politics, sport, and tech using MiniBatchKMeans and TF-IDF features. (2023-10-10, Jupyter Notebook, 0KB, 下载0次)
以「台北城史」为语言材料，总结其中记载的人的生活。
Use 「History of City of Taipei」 as laguage material, summarize the lives of people recorded in it. (2019-02-02, Jupyter Notebook, 40498KB, 下载0次)
无监督LDA模型，用于对8K及其企业事件类型进行分类。
Unsupervised LDA model to categorize 8Ks and their corporate event types. (2020-06-05, Jupyter Notebook, 2384KB, 下载0次)
SearchBetter:小型企业搜索引擎的查询重写（哈佛大学研究项目）
SearchBetter: query rewriting for search engines on small corpuses (Harvard research project) (2017-07-15, Jupyter Notebook, 6695KB, 下载0次)
...类别，即商业、娱乐、政治、体育和科技。使用这种方法，我们能够找到...
Article classification and summarization have a genuinely backhanded connection as article classification fall into classification issues rather than summarization, where it is treated as an issue of semantics. A significant piece of the summarization procedure is the recognition of the point or subjects that are examined in an irregular (2020-08-04, Jupyter Notebook, 5247KB, 下载0次)