Definitional Starting Points: Word2vec and Convolutional Neural Network

Based on recent legal technology announcements highlighting two important but sparingly used terms (Word2vec and Convolutional Neural Network), the following “starting points” are provided for a definitional overview of these terms along with additional links for context and reference.


Based on recent legal technology announcements highlighting two important but sparingly used terms (Word2vec and Convolutional Neural Network), the following “starting points” are provided for a definitional overview of these terms along with additional links for context and reference.

STARTING POINT #1

What is Word2vec?

Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.

Word2vec was created by a team of researchers led by Tomas Mikolov at Google. The algorithm has been subsequently analyzed and explained by other researchers. Embedding vectors created using the Word2vec algorithm have many advantages compared to earlier algorithms like Latent Semantic Analysis.

Read the complete article on Word2vec 

STARTING POINT #2

What is a Convolutional Neural Network?

In machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. Individual cortical neurons respond to stimuli in a restricted region of space known as the receptive field. The receptive fields of different neurons partially overlap such that they tile the visual field. The response of an individual neuron to stimuli within its receptive field can be approximated mathematically by a convolution operation. Convolutional networks were inspired by biological processes and are variations of multilayer perceptrons designed to use minimal amounts of preprocessing. They have wide applications in image and video recognition, recommender systems and natural language processing.

Read the complete article on Convolutional Neural Network

Source: Wikipedia

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