word-sense-disambiguation | Incorporating Dictionary Knowledge into Neural | Graph Database library

Transfer learning, particularly models like Allen AI’s ELMO, OpenAI’s Open-GPT, and Google’s BERT allowed researchers to smash multiple benchmarks with minimal task-specific fine-tuning and provided the rest of the NLP community with pretrained models that could easily (with less data and less compute time) be fine-tuned and implemented to produce state of the art results.

these representations will help you accuratley retrieve results matching the customer's intent and contextual meaning(), even if there's no keyword or phrase overlap.

To start off, embeddings are simply (moderately) low dimensional representations of a point in a higher dimensional vector space.

By translating a word to an embedding it becomes possible to model the semantic importance of a word in a numeric form and thus perform mathematical operations on it.

When this was first possible by the word2vec model it was an amazing breakthrough. From there, many more advanced models surfaced which not only captured a static semantic meaning but also a contextualized meaning. For instance, consider the two sentences below: