Python uses NLTK to implement part of speech tagging: tagging the part of speech of each word, such as nouns, verbs, adjectives, etc

Before using NLTK for part of speech tagging, some preparation work and environment building are required. Firstly, ensure that you have installed the latest version of Python and have the ability to use the pip package management tool. Next, you need to install the NLTK class library. You can install using the following command: pip install nltk Then, you need to download some necessary datasets. In this example, we need to download the annotated dataset in English. Use the following command to download: python -m nltk.downloader averaged_perceptron_tagger After the download is completed, we can start implementing part of speech tagging. Sample data: > "Apple is looking at buying U.K. startup for $1 billion." In this example, we will label each word with a part of speech. The following is the complete sample source code: python import nltk #Decompose text into words text = nltk.word_tokenize("Apple is looking at buying U.K. startup for $1 billion.") #Perform part of speech tagging tagged = nltk.pos_tag(text) #Output part of speech annotation results print(tagged) Output results: [('Apple', 'NNP'), ('is', 'VBZ'), ('looking', 'VBG'), ('at', 'IN'), ('buying', 'VBG'), ('U.K.', 'NNP'), ('startup', 'NN'), ('for', 'IN'), ('$1', 'CD'), ('billion', 'CD'), ('.', '.')] In the output result, each word is followed by an abbreviation representing the part of speech. For example, 'Apple' is marked as' NNP 'to indicate that it is a proper noun, and' is' is marked as' VBZ 'to indicate that it is a verb. Here, we use 'nltk. pos'_ The tag() ` function is used to perform part of speech tagging. It takes a list containing words as input and returns a tuple list containing each word and its part of speech label. It is worth noting that this is just a simple example demonstrating how to use NLTK for part of speech tagging. In practical applications, more preprocessing and post-processing steps may be required to process text data.