soskek/dynamic_neural_text_model
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse, Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui, IJCNLP 2017
This project helps natural language processing (NLP) researchers and engineers better understand and generate text by dynamically learning the meaning of words and entities within a conversation or document. It takes raw text as input and produces more accurate language models that can track and represent how the meaning of specific words or names evolves throughout a discourse. This is particularly useful for those building systems that need to comprehend nuanced text.
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Use this if you are working on advanced NLP tasks and need a language model that can adaptively represent the meaning of unfamiliar words and entities based on their context within a given text.
Not ideal if you are looking for a simple, off-the-shelf solution for basic text processing or if your primary goal is not focused on the dynamic representation of word and entity meanings.
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Sep 09, 2017
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