Owl API is a powerful word similarity service using advanced text clustering techniques and various word2vec models. In this article, I want to introduce a powerful word similarity API, named Owl, that helps you extract the most similar words, to target words.
In natural language processing or NLP, most tasks are built based on extracting semantics of words, sentences, or documents. For example, we build an extractive summarization by extracting the semantics of sentences and clustering them based on their significance in a document. Or, we build topic modeling solutions by extracting the word groups that characterize a set of documents the best.The first element of language with a meaning is a word. So, you can guess how important is to correctly extract the semantic relations of words in NLP tasks. One of the most powerful tools to extract word semantics is word2vec models. These word2vec models are trained for different contexts and provide high-dimension vector representations of words. Nevertheless, the analysis is sophisticated due to the high-dimension space and results yet lack quality.In this article, I want to introduce a powerful word similarity API, named Owl, that helps you extract the most similar words, to target words. This API extracts the most similar words with more granularity comparing to the current solutions that are highly needed for NLP projects.
This Owl API uses various word2vec models and advanced text clustering techniques to create a better granularity comparing to the industry standards. In fact, it uses the largest word2vec English model created by spaCy (i.e., en-core-web-lg) for the general context and uses one of the word2vec models created at Stanford University (i.e., glove-wiki-gigaword-300) for the news context.Here, I compare the results of the most-similar services introduced by these three models. This helps you find out why** Owl is your answer**.
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