Python is a powerful object-oriented programming (OOP) language that is widely used in the field of artificial intelligence. It is precisely because of its practicality that large technology companies led by Google have developed code libraries such as Tensorflow to help people use powerful machine learning algorithms and models to achieve various application purposes, including various "sign language" parser, motorcycle helmet detector, and various item recognizers.
NLP (natural language processing) is the general term for all artificial intelligence activities related to understanding and manipulating natural language. In Python, there is a machine learning model called Transformers that can be used to take text and decompose it into different components to identify important parts of it. Next, let's discuss how Transformer, as a deep learning model, parses text.
1. How to use the Transformer library to parse text in Python?
Before getting started, you need to have a Google account. In order to save the trouble of installing Python, its dependencies, and IDE (integrated development environment, integrated development environment) on your own computer, we use a free cloud service environment-Google Colab notebook to collaborate with different people using Python . At the same time, since the AI code base itself is large in size and has many dependencies, the use of the cloud environment can effectively save their hard disk space occupation.
1. Install the required libraries
First, we need to install the following four code libraries. Open a Colab notebook and enter the following in the first code cell:
!pip install transformers
!pip install torch
!pip install sentencepiece
!pip install newspaper3k
Before we continue, let's understand these commands a little bit. as you know:
- "Transformers" are deep learning models that can be used to parse text.
- "Torch" provides algorithms for deep learning.
- "Sentencepeice" can be used to "tokenize" (decompose) text.
- "Newspaper3k" is a web scraping library that can be used to import articles (text content) from the internet.
At this point, your screen will display the following:
2. Import articles
In order to import an article, you must provide its corresponding URL. Next, you need to enter the following command to download and parse the article so that we can further tag it later.
After that's done, we'll move on to step 3.
3. Tag Articles
We need to import the automatic tokenizer from the conversion library, and then use the T5 model (T5 is a machine learning model), which can be used for text-to-text conversion (here we can use it for parsing), and then Generate parsed text. The image below shows the code that needs to be entered for this effect.
4. Parse the article
In order to parse this article, you need to create a specific function. This function accepts tagged articles and parses each sentence individually. It then reconnects the sentences together before outputting them.
The image below shows the output of the parsed text:
You can manually copy it into a text file for better readability.
This is one way of parsing text in Python using NLP libraries. It's a rather complicated and cumbersome way to do it, though, especially for those new to AI and Python. At this point, you must think, are there some online analysis tools to achieve this purpose?
2. Free tools available for online analysis
1.Prepostseo
Prepostseo provides very useful parsing tools that can be used for various purposes. Since it's free to use, you don't need to sign up for any kind of account to start using it smoothly.
When using the tool, you can use the following three modes for free:
- simple mode
- advanced mode
- smooth mode
Among them, in the simple mode, the tool will only perform some simple synonyms, that is, multiple words will be replaced by some synonyms.
And advanced mode changes more than just words and parsed results. If you don't like the result it gives by default, you can look at its modifiable place and replace it with another synonym.
Fluency patterns change not only words, but phrases, sentence structure, and tone. However, it does not provide an option to edit the output.
It can be seen that smooth mode and advanced mode are more effective modes. To import custom content, you can upload the document to be parsed, or simply copy and paste the text into the input field, and download its output after the parsing process is complete.
The only downside to this tool is that there will be advertisements on its pages.
2.Linguix
Linguix is another free parser that can be used without registration. It is very user-friendly as it does not have any ads on its pages.
Although Linguix doesn't offer multiple modes, when you're parsing a sentence, you'll get multiple suggestions, not just one. Given that all suggestions have the potential to produce different changes to the given text, you can choose the one that suits you best.
The operation method of this tool is relatively simple. You only need to write the text to be parsed in the input box, and then select it in a highlighted way to get corresponding pop-up suggestions sentence by sentence.
The only downside to this tool: you can only parse five sentences at a time.
3. Paraphrase
Paraphraser.io is also an online toolkit with many content optimization tools. As the name suggests, it is mainly aimed at the field of analysis.
The tool is also free to use without registration. However, similar to the aforementioned Prepostseo, you may also be troubled by its advertisements during use. Currently, it offers two free modes: standard mode and smooth mode. Among them, the standard mode only uses synonyms to replace some words, and keeps the structure of the whole sentence unchanged. In addition to replacing words and phrases, fluency mode also changes the structure of sentences, thereby making the text more readable.
Ads aside, another downside of the tool: you can only parse up to 500 words at a time.
3. Summary
To sum up, when using the NLP library to parse text in Python, we can use various models of artificial intelligence and deep learning to achieve conversion. You can use the powerful cloud service functions of Google Colab and use the Transformer library to complete such heavy tasks; you can also choose various modes of various online analysis tools to rewrite the text in different ways. Also, most of these tools are free and require no registration.
See more great tools
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