extract from a story

Extracting text from a web page usually requires a text analysis tool such as the developer tools of Google Chromeor the developer tools of MicrosoftEdge. These tools provide some text analysis functions to view the text content of the web page and extract it. The specific steps were as follows: 1 Open the webpage and use the developer tools to view the webpage source code. 2. You can view all of the web page's elements and text in the developer tools. 3 Use a text editor (such as Sublime Text, Atom, etc.) to open the source code of the web page and use the "check elements" function in the text analysis tool to find the webpage elements. 4. If you find the element, you can check its attributes and extract the text content. 5 You can use the "Search" function of the text analysis tool to find specific text content such as keywords, titles, passages, etc. It should be noted that extracting the text from the webpage requires a certain understanding of the webpage content in order to accurately locate and extract the required content.

To extract the novel text on the web page, you need to use a specific text analysis tool such as a Web mining tool or a text analysis software. These tools can scan the entire web page and extract the text content of the novel. Specifically, you can use the following steps to extract the novel text on the web page: 1 Use Web mining tools such as Python's Request library and BeautifulSoup library to get the content of the web page. 2. Use text analysis tools such as Python's NLTL library or Python's Scrapy library to analyze the web content and extract the novel text. 3. Store the extracted novel content into a text file such as a dsv file. It should be noted that the content structure and format of different web pages may be different, so there may be some differences in the extracted novel text. Therefore, when extracting the novel text, it was necessary to analyze and extract the webpage.

There was a classic story in Journey to the West called "Three Beats on White Bone Demon". The story described the battle between Sun Wukong and the White Bone Demon. The White Bone Demon was a hybrid between humans and demons, and because she wanted to seize Tang Sanzang's body, a series of events were triggered. Sun Wukong and the others went to India to learn from the scriptures and met Bai Gujing on the way. Sun Wukong first used a black magic to turn White Bone Demon into a mortal, but White Bone Demon countered and turned him into a pile of skeletons. After that, Sun Wukong, Tang Sanzang, Zhu Bajie, and Monk Sand joined forces to fight against the White Bone Demon. Sun Wukong used the Jingu Staff to defeat the White Bone Demon. Zhu Bajie and Monk Sand used their weapons to attack the White Bone Demon but were defeated in the end. In the end, Tang Sanzang realized his mistake and decided to give up the demon's body and take back the scriptures. Sun Wukong and the others had also successfully completed the mission.

Extracting text usually requires the use of natural language processing techniques. Here are some common methods: 1 Bag-of-Words Model: Transform the text into a vocabulary and select the most frequently appearing words as keywords. This method is suitable for the situation where the text volume is small and can quickly identify keywords in the text. 2. The TF-IDF (Term-frequency-inverse Document Frequency Model): Transform the text into a word frequency and calculate the importance of each word. This method is suitable for the situation where the text volume is large, and it can identify high-frequency words and keywords in the text. 3. Word sense disambiguation: classify the words in the text to better identify keywords. This method requires the text to be divided into words and then classify each word using the part-of-speech tagging tool. 4. Stop list (stop list): This includes some common stop words and phrases such as "the","of","and","belong", etc. These words are usually not used to express specific meanings but can be identified as keywords. Each of the above methods had its advantages and disadvantages. The specific method to use depended on the application scenario and data.

To extract the text from the webpage, you need to use some web crawling techniques. For details, you can refer to the following steps: 1 Obtain the source code of the webpage: You can use the "View" menu or the "developer tools" option of various browser to obtain the source code of the webpage. 2. Parse the source code of the webpage: Use regular expressions or other analysis techniques to analyze the source code of the webpage and extract the required information such as text content. 3. Store text data: Store the extracted text data in a local or server data store for subsequent analysis and use. Some commonly used web crawling framework included:Python's Request and Beautiful Soup's Scikit-learn and Jsoup. Before using these framework, you need to understand the relevant programming knowledge and crawling technology, and be familiar with the commonly used data structures and algorithms.

Excerpt from the novel "Crusoe".

Extracting data from irregular locations in the web text usually requires some image processing and data analysis tools. For details, you can refer to the following methods: 1. Using a crawling tool: Extracting data from a web page usually requires the use of a crawling tool. You can use Python and other programming languages to write a crawling program to traverse the web page and extract the required data. Commonly used crawling tools included Scrapy and Beautiful Soup. 2. Use image processing tools: Image processing tools can help to extract irregular data from the webpage. For example, use software such as Photoshop to select the data that needs to be extracted and then use image processing tools to crop, scale, rotate, and other operations. 3. Use natural language processing tools: Natural language processing tools can help convert the text in the web page into data. For example, use Python's NLTL and SpaCy to process and analyze the text in the web page. 4. Use machine learning algorithms: Machine learning algorithms can help automatically extract irregular data from web pages. For example, using neural networks or support matrix machines to classify or cluster the text in web pages. No matter which method was used, the required data needed to be pre-processed and cleaned to ensure the accuracy and integrity of the extracted data. At the same time, he also needed to understand the application scenarios and limitations of the extracted data in order to choose the appropriate methods and tools.

Yi language was a programming language that could not directly access web pages. To extract text from a web page, a third-party library or tool had to be used. The following is the sample code for extracting web page text using Python: ```python import requests from bs4 import BeautifulSoup Url =>> response = requiestsget (uri) #Send a request and get a response if responsestatus_code == 200: #Judge whether the request is successful soup = BeautifulSoup(responsetext htmmlParser) #Parse the browser with BeautifulSoup text = soupselect_one(text a)text #select all <a>the text in the tag and get its text attribute print(text) #Print the extracted text else: print(Request failed) ``` The above code uses the requests library to send a request to get the content of the web page, uses the BeautifulSoup library to analyze <a>the text in all the tags, and obtains the text attribute. Finally, the extracted text is output. You can modify the code to extract different text content as needed.

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To extract the script text from the web page, you can use the function provided by the browser to view the source code of the web page and then use the corresponding text editor to edit it. The following are some common methods: 1 Use the browser's "View" menu and select the "Source" option to open the source code of the web page. You can see all of the source code, including all of the scripts. You can use the "Find" and "Substitute" functions in the text editor to extract the code from the script element. 2. Use the built-in function of Easy Language to view the source code of the webpage, the System WebUIWebControllers WebPart WebControl Get ()Script. This function returns the string values of all the script elements in the web page, which can be used directly. 3. Use the built-in function of Easy Language, System IOStringReader Read To End (), to read the source code of the webpage into a string variable and then process the text. It should be noted that extracting the script text may require a certain understanding of the script language in order to correctly understand and extract the code. At the same time, due to the different script language and format in the webpage, the extracted script text may also be different.