Legal Applications of Neural Word Embeddings
<p>A fundamental issue with LegalTech is that words — the basic currency of all legal documentation — are a form of unstructured data that cannot be intuitively understood by machines. Therefore, in order to process textual documents, words have to be represented by vectors of real numbers.</p>
<p>Traditionally, methods like bag-of-words (BoW) map word tokens/n-grams to term frequency vectors, which represent the number of times a word has appeared in the document. Using one-hot encoding, each word token/n-gram is represented by a vector element and marked 0, 1, 2 etc depending on whether or the number of times that a word is present in the document. This means that if a word is absent in the corpus vocabulary, the element will be marked 0, and if it is present once, the element will be marked 1 etc.</p>
<p><a href="https://towardsdatascience.com/legal-applications-of-neural-word-embeddings-556b7515012f"><strong>Read More</strong></a></p>