Capturing semantic meanings using deep learning

Word embedding is an alternative technique in NLP, whereby words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size, and the similarities between the vectors correlate with the words' semantic similarity. Many different types of models were proposed for representing words as continuous vectors, including latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). In the skip-gram model, instead of using the surrounding words to predict the center word, it uses the center word to predict the surrounding words (see Figure 3). Deep Solutions delivers end-to-end software solutions based on deep learning innovative algorithms for computer vision, natural language processing, anomaly detection, recommendation systems, and more.