In our current day and age, natural language processing systems process unstructured data and convert it into structured data to be used for artificial intelligence processes. One of the most important steps of natural language processing is word embedding, which is a process whereby words from a corpus are transformed into vectors of real numbers. This transformation is important because it allows natural language processing applications to utilize machine learning models and algorithms for text analysis. In particular, word embedding enables us to effectively perform text classification tasks, such as sentiment analysis and topic classification.
In this article, we’ll provide a practical overview of using pretrained word embeddings for text classification. We’ll also provide a code example of using pre-trained word embeddings in Python. So, without further ado, let’s dive into this concept!
Before we dive into pretrained word embeddings for text classification, let’s take a few moments to understand what word embeddings are and how they work.
Word embedding is the process of transforming words or phrases into numerical vectors, by mapping words to points in a multi-dimensional space. This process enables us to use the words in various machine learning applications and algorithms.
Word embedding models, such as Word2Vec and GloVe, learn the word embedding by analyzing large corpora of text. The Word2Vec and GloVe models generate word embedding vectors by analyzing the context of words and assigning a lower-dimensional vector to each word or phrase.
Using word embedding models has numerous advantages. One of the biggest advantages is that the vectors generated for words are able to capture the semantic meaning of words and phrases. This helps us in performing accurate sentiment analysis and text classification.
In some cases, you may not have to train your own word embedding model. Instead, you can use a pretrained word embedding model. As the name suggests, in pretrained word embedding, you’re using a previously trained word embedding model, instead of training your own.
Pretrained word embeddings are useful if you don’t have a large corpus of text to train your own model or if the data available isn’t adequate to get accurate results. You can also use pretrained word embeddings if you’re performing a task which requires a large number of words, such as text classification.
Text classification is the process of analyzing and sorting text into various categories. Text classification requires a model that is able to accurately differentiate between different classes. To do this, the model needs to be able to capture the meaning of words; this is what word embedding does.
By using a pretrained word embedding model, we can get the vector representations of words and phrases and use them for text classification tasks. We can use Word2Vec and GloVe models for text classification tasks.
In this section, we’ll provide a Python code example for using pretrained word embeddings for text classification.
We’ll be using Keras, a popular deep learning library, and GloVe, a pretrained word embedding model.
First, let’s define the imports required to execute this program:
import numpy as np from keras.layers import Embedding, Flatten, Dense from keras.datasets import imdb from keras.models import Sequential from keras.preprocessing import sequence
Next, we have to load the pre-trained word embedding model. We’ll use the GloVe model which has been pre-trained on the large Wikipedia corpus.
embedding_layer = Embedding(vocab_size, 300, weights = [embedding_matrix], input_length = maxlen, trainable = False)
Now, let’s define our model:
model = Sequential() model.add(embedding_layer) model.add(Flatten()) model.add(Dense(1, activation = 'sigmoid'))
Finally, let’s compile the model and train it:
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) model.fit( x_train, y_train, epochs = 5, batch_size = 32, validation_data = (x_test, y_test))
That’s it! We now have a model that is able to accurately classify text using pretrained word embedding.
In this article, we looked at pretrained word embeddings and how they can be used for text classification. We also provided a Python code example of how to use pretrained word embeddings for text classification.
With this knowledge, you’ll be able to build your own text classification applications using pretrained word embeddings. Good luck!