NLTK Stopwords 停用词

HanLP: Han Language Processing | | | | | 面向生产环境的多语种自然语言处理工具包，基于PyTorch和TensorFlow 2.x双引擎，目标是普及落地最前沿的NLP技术。HanLP具备功能完善、性能高效、架构清晰、语料时新、可自定义的特点。 借助世界上最大的多语种语料库，HanLP2.1支持包括简繁中英日俄法德在内的104种语言上的10种联合任务：分词（粗分、细分2个标准，强制、合并、校正3种）、词性标注（PKU、863、CTB、UD四套词性规范）、命名实体识别（PKU、MSRA、OntoNotes三套规范）、依存句法分析（SD、UD规范）、成分句法分析、语义依存分析（SemEval16、DM、PAS、PSD四套规范）、语义角色标注、词干提取、词法语法特征提取、抽象意义表示（AMR）。 量体裁衣，HanLP提供RESTful和nati

BosonNLP_sentiment_score 知网Hownet 清华大学——李军中文褒贬义词典 台湾大学NTUSD 其他词典和分类

下载解压后，将config.json文件下的“config_path”的值里面那两个点和斜杠去掉， 即"config_path": "configs/cnn_50_100_512_4096_sample.json" 然后把整个文件夹拖进项目里即可使用

实验 词汇分析 1）使用任意分词方法编写算法实现汉语自动分词程序； 2）编写直接调用分词工具(jieba分词，中科院分词等)进行分词的程序; 3）用两种方法，给出至少50个句子的分词结果（以附件形式）； 4）分别计算出两种分词结果的正确率，给出计算依据。

实验 句法分析 1）使用至少两种依存句法分析工具(HanLP，Stanford CoreNLP 等)编写句法 程序; 2）给出至少20 个句子的分析结果，以结构化方式存储（json 或xml）; 3）分别计算出不同方法结果的正确率，并对比不同方法的差异。 4）对结果进行可视化（选做）

这是缩减版数据集，4000多条。 工单分类博文见 https://blog.csdn.net/kobeyu652453/article/details/106551131

java版飞机大战源码 awesome-chinese-nlp A curated list of resources for NLP (Natural Language Processing) for Chinese 中文自然语言处理相关资料 图片来自复旦大学邱锡鹏教授 Contents 列表 1. 2. 3. 4. 5. Chinese NLP Toolkits 中文NLP工具 Toolkits 综合NLP工具包 by 清华 (C++/Java/Python) by 中科院 (Java) by 哈工大 (C++) LTP的python封装 by 复旦 (Java) by 百度 Baidu's open-source lexical analysis tool for Chinese, including word segmentation, part-of-speech tagging & named entity recognition. (Java) (Python) 一款轻量级的 NLP 处理套件。 (Python) Python library for processing

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

简历分析仪 使用NLP根据技能和经验分析使用NLP进行项目分配的员工的简历/简历。