In this post, we explain what document embedding is, why it is useful, and show its usage on the classification example without coding. For the analysis, we will use the Orange open-source tool.
Text is described by the sequence of character. Since every machine learning algorithm needs numbers, we need to transform the text into vectors of real numbers before we can continue with the analysis. To do this, we can use various approaches. The most known approach before the evolution of deep learning was the bag of words which is still widely used because of its advantages. The recent boom in the deep learning brought us new approaches such as word and document embeddings. In this post, we explain what document embedding is, why it is useful, and show its usage on the classification example without coding. For the analysis, we will use the Orange open-source tool.
Before we can understand document embeddings, we need to understand the concept of word embeddings. Word embedding is a representation of a word in multidimensional space such that words with similar meanings have similar embedding. It means that each word is mapped to the vector of real numbers that represent the word. Embedding models are mostly based on neural networks.
Document embedding is usually computed from the word embeddings in two steps. First, each word in the document is embedded with the word embedding then word embeddings are aggregated. The most common type of aggregation is the average over each dimension.
Compared to bag-of-words, which counts the number of appearances of each token (word) in the document, embeddings have two main advantages:
The shortcoming of the embedders is that they are difficult to understand. For example, when we use a bag-of-words, we can easily observe which tokens are important for classification since tokens themselves are features. In the case of document embeddings, features are numbers which are not understandable to human by themselves.
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