Python uses NLTK to generate text or evaluate its fluency

Preparation work: 1. Install Python: Download and install the latest version of Python installation package from the https://www.python.org/downloads/ Obtain. 2. Install NLTK: Run 'pip install nltk' from the command line to install NLTK. 3. Download NLTK dataset: Run the following code in Python's interactive environment to download the necessary dataset: python import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger') nltk.download('wordnet') Dependent class libraries: NLTK (Natural Language Toolkit) is a powerful Python library, which provides many functions of text processing and Natural language processing. Dataset used: In this example, we will use some sample data provided by NLTK. Sample code: This sample code is based on NLTK for text smoothness evaluation. It uses some language models to estimate the smoothness of the text. python import nltk from nltk.util import ngrams #Read Text Data text = "I am happy because I am learning" #Tokenization tokens = nltk.word_tokenize(text) #Create n-grams n = 2 grams = ngrams(tokens, n) #Calculate the frequency of n-grams frequency = nltk.FreqDist(grams) #Calculate fluency score score = 1 for gram in grams: score *= frequency[gram]/frequency[gram[:-1]] Print ("Fluency score:", score) Source code explanation: 1. Import the 'nltk' module and the 'ngrams' method from' nltk. util '. 2. Provide text data to evaluate fluency. 3. Use 'nltk. word'_ Tokenize 'Splits text into words. 4. Convert the tokenized text into n-grams by calling the 'ngrams' method. 5. Use 'nltk. FreqDist' to calculate the frequency of n-grams. 6. Calculate the fluency score by dividing the frequency of the previous n-gram by the frequency of use. Please note: -This example is a simple fluency assessment based on n-grams. Depending on specific needs, it may be necessary to use more complex language models to complete more accurate fluency assessments. -The sample code only uses a simple sentence as sample data, which can be replaced with other text data as needed.