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Course Introduction：This article introduces how to use RegexpTokenizer in the NLTK library to customize word segmentation rules, which can extract all words in the text as tokens, and also use the specified phrase as a separate token. By modifying the regular expression and setting gaps=False, flexible word segmentation requirements are achieved to better process text data.

2025-08-17 comment 0  460

Course Introduction：NLTK is suitable for beginners of NLP. It is simple to install and provides a complete corpus and clear interface. It can complete basic tasks such as word segmentation, part-of-speech annotation, naming entity recognition, etc. The usage process includes installing pipinstallnltk, downloading corpus such as punkt and wordnet, importing modules and calling functions to process text, such as word_tokenize to implement word segmentation, pos_tag for part-of-speech annotation; it also supports stop word filtering, word form restoration and other functions, but attention should be paid to problems such as text preprocessing and weak Chinese support. It is recommended to use spaCy or transformers for large-scale processing.

2025-07-24 comment 0  310

Course Introduction：It is feasible to use Python and NLTK as chatbots, but the goals and methods need to be clarified. 1. Install Python and NLTK and download the necessary corpus such as punkt, stopwords and wordnet. 2. The implementation process includes text preprocessing (word segmentation, stop word deactivation, word shape restoration), intent recognition or keyword matching, and response generation. 3. Simple response can be achieved through keyword matching, or classification models can be trained to improve the effect. 4. Extension directions include introducing more powerful NLP tools such as spaCy or Transformers, maintaining Q&A databases, and avoiding too much hardcoded logic. In short, it is suitable for introductory and small projects, with low deployment costs but strong controllability.

2025-07-21 comment 0  658

Course Introduction：The core of the natural language understanding (NLU) system is to enable machines to "understand" human language. Python provides comprehensive support from text preprocessing to model training to deployment and launch. 1. Text preprocessing includes data cleaning and feature extraction. Common tools are nltk, spaCy and sklearn, which involve removing punctuation, stop words, word segmentation, stemming or word shape restoration. 2. Model selection depends on the task type. Traditional methods such as TF-IDF combined with SVM are suitable for getting started. Deep learning methods such as BERT are more suitable for complex semantic tasks and can be implemented through the transformers library. 3. Interfaces can be built using Flask or FastAPI during the deployment stage, combined with Docker containers and ONNX.

2025-07-23 comment 0  389

Course Introduction：Use standard log packages to handle simple scenarios, suitable for small tools or CLI applications; 2. Recommend structured log libraries such as logrus, zap or Go1.21 slog in the production environment, which support log levels, structured fields and JSON output; 3. Add context to the logs such as request ID, user ID and other information, which can be implemented through middleware; 4. Pay attention to log rotation and management, set appropriate log levels and combine tools to implement file segmentation. The article points out that the Go standard library's log package is suitable for simple needs, but the production environment should use a third-party library that supports structured logs, and emphasizes the importance of adding contextual information to the logs, and recommends that log output be managed reasonably to improve maintainability.

2025-07-22 comment 0  541

Course Elementary  13884

Course Introduction：Scala Tutorial Scala is a multi-paradigm programming language, designed to integrate various features of object-oriented programming and functional programming.

Course Elementary  82439

Course Introduction："CSS Online Manual" is the official CSS online reference manual. This CSS online development manual contains various CSS properties, definitions, usage methods, example operations, etc. It is an indispensable online query manual for WEB programming learners and developers! CSS: Cascading Style Sheets (English full name: Cascading Style Sheets) is an application used to express HTML (Standard Universal Markup Language).

Course Elementary  13234

Course Introduction：SVG is a markup language for vector graphics in HTML5. It maintains powerful drawing capabilities and at the same time has a very high-end interface to operate graphics by directly operating Dom nodes. This "SVG Tutorial" is intended to allow students to master the SVG language and some of its corresponding APIs, combined with the knowledge of 2D drawing, so that students can render and control complex graphics on the page.

Course Elementary  24692

Course Introduction：In the "AngularJS Chinese Reference Manual", AngularJS extends HTML with new attributes and expressions. AngularJS can build a single page application (SPAs: Single Page Applications). AngularJS is very easy to learn.

Course Elementary  27536

Course Introduction：Go is a new language, a concurrent, garbage-collected, fast-compiled language. It can compile a large Go program in a few seconds on a single computer. Go provides a model for software construction that makes dependency analysis easier and avoids most C-style include files and library headers. Go is a statically typed language, and its type system has no hierarchy. Therefore users do not need to spend time defining relationships between types, which feels more lightweight than typical object-oriented languages. Go is a completely garbage-collected language and provides basic support for concurrent execution and communication. By its design, Go is intended to provide a method for constructing system software on multi-core machines.