Word embeddings has revolutionized the field of NLP. At its core, word embeddings are word vectors that each correspond to a single word such that the vectors "mean" the words. This can be demonstrated by certain phenomena such as the vector for king - queen = boy - girl. Word vectors are used to build everything from recommendation engines to chatbots that actually understand the English language.
Another point worth considering is how we obtain word embeddings as no two sets of word embeddings are the same. Word embeddings aren't random; they're generated by training a neural network. A recent powerful word embedding implementation comes from Google named Word2Vec which is trained by predicting words that appear next to other words in a language. For example, for the word "cat", the neural network will predict the words "kitten" and "feline". This intuition of words appearing "near" each other allows us to place them in vector space.
However, it is an industry standard to use the pre-trained models of other large corporations such as Google in order to quickly prototype and to simplify deployment processes. We can download Google's Word2Vec pre-trained word embeddings by running the following command in our working directory:
wget http://magnitude.plasticity.ai/word2vec/GoogleNews-vectors-negative300.magnitudeThe word embedding model we downloaded is in a .magnitude format. This format allows us to query the model efficiently using SQL, and is therefore the optimal embedding format for production servers. Since we need to be able to read the .magnitude format, we'll install the pymagnitude package. We'll also install flask to later serve the deep learning predictions made by the model.
pip3 install pymagnitude flaskSee more about how to create and deploy a pre-trained Word2Vec deep learning REST API.
In recent years, researchers further explored deep neural networks (DNNs) in regards to image classification and speech recognition. Language learning and representation via DNNs gradually became a new research trend. However, due to the flexibility of human languages and the complexity involved in the abstraction of semantic information, the DNN model is facing challenges in implementing language representation and learning.
Researchers are increasingly interested in the application of the deep learning model for natural language processing (NLP), focusing on the representation and learning of words, sentences, articles, and relevant applications. For example, Bengio et al. obtained a new vector image called word embedding or word vector using the neural network model [27]. This vector is a low-dimensional, dense, and continuous vector representation, and contains semantic and grammatical information of the words. At present, word vector representation influences the implementation of most neural network based NLP methods.
In the past, the recommendation system algorithms for the Tmall homepage focused on optimizing relevant recommendations. Now, the recommendation system not only considers the relevance of recommendation results but also serve to optimize the discovery and diversity of recommendation results. Efficiency and user experience equally are now equally the two main optimization objectives of the Tmall homepage nowadays. New technology such as graph embeddings, transformers, deep learning, and knowledge graphs have been applied to the recommendation system for the Tmall homepage. All of these changes have helped to ensure a double-digit click-through rate (CTR) growth and a double-digit fatigue reduction in different scenarios.
Graph embedding is a machine learning technology that projects complex networks to low-dimensional space. Typically, this technology vectorizes network nodes and ensures that the vector similarity between nodes is close to the multi-dimensional similarity between original nodes in terms of the network structure, neighborship, and metadata.
As for sorting models, in order to resolve the preceding problems of the Wide and Deep Learning (WDL) and DIN models and enable the transformer to effectively process word sequences in NLP tasks, we proposed the behavior sequence transformer (BST) model. This model uses the transformer to model users' behavior sequences and to learn the correlation between user behavior sequences and their correlation with scoring items.
Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine Learning Platform for AI also provides text processing components for NLP, including word splitting, deprecated word filtering, LDA, TF-IDF, and text summarization.
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