Natural language processing example in Python: word vectors

Python Natural Language Processing (NLP) is a widely used technique for extracting and analyzing meaningful information from human language data. One of the important NLP applications is word embeddings, which is a technique that converts words into numeric vectors, representing the semantics of words as real values ​​in vector space.

In this article, we will learn how to use Python and the NLP library to create a word vector model and perform some basic analysis on it.

Install Python NLP library
We will use the gensim library in Python, which is a library specifically used for NLP. Before using it, you first need to install gensim on your local computer. We can install gensim in the terminal using the following command:

pip install gensim

Prepare data
Before creating word vectors, we need to prepare some text data as input. In this example, we will use the classic novel from Project Gutenberg as our input text.

We will use the following code to download and import the Project Gutenberg library:

!pip install gutenberg

from gutenberg.acquire import load_etext
from gutenberg.cleanup import strip_headers
text = strip_headers(load_etext(2701)).strip()

Here, we remove the top information and header of the novel by calling the strip_headers function. Now, we are ready to feed this text into the word vector model.

Create a word vector model
To create a word vector using Python, we need to perform the following steps:

Convert raw text to a word list
Use a word list to train a word vector model
In the following code, we split the text into words, build a vocabulary, encode the words into integers, and train a word vector model using the gensim library.

from gensim.models import Word2Vec
import nltk
nltk.download('punkt')

raw_sentences = nltk.sent_tokenize(text)
sentences = [nltk. word_tokenize(sentence) for sentence in raw_sentences]
model = Word2Vec(sentences, min_count=1)

First, we use the sent_tokenize function in the nltk library to divide the text into sentences.

We then use nltk’s word_tokenize function to break the sentence into words. This will return a nested list of words.

The Word2Vec model uses a list of nested words as input and learns word vectors based on their co-occurrence relationships. The min_count parameter specifies the minimum number of occurrences of a word before it is considered.

Training the model takes some time, depending on the size of the input data set and the performance of your computer.

Model Analysis
We can use the following code to analyze the word vector model:

Find other words that are most similar to the word

model.wv.most_similar('monster ')

Find the word vector

model.wv['monster']

View the size of the vocabulary

len(model.wv.vocab)

Save the model to disk

model.save('model.bin')

Load the model from disk

model = Word2Vec.load( 'model.bin')

Here, we first use the most_similar function to find other words that are most similar to the word monster. Results include word and similarity scores.

Next, we use the wv attribute in the word vector description to find the vector representation of the word monster.

len(model.wv.vocab) checks the size of the vocabulary in the model. Finally, we use the save and load functions to save and load the model.

Conclusion
In this article, we learned how to create a word vector model using Python and the gensim library. We saw how to convert text into a list of words and use this data to train a word vector model. Finally, we also learned how to use a model to find the words that are most similar to a given word.

Word vectors are an important topic in NLP. Through this article, you have learned how to use the NLP library in Python for word vector analysis. I hope this will be helpful to you.

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