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Not a paper, but NLP extraction into knowledge graph representations do exist and are in use today. For example, here's a general purpose NLP model that links o
by jeromechoo 4y ago
Not a paper, but NLP extraction into knowledge graph representations do exist and are in use today. For example, here's a general purpose NLP model that links organization and people entities (among others) to each other based on factual relationships described in the text — https://demo.nl.diffbot.com https://demo.nl.diffbot.com
This semantic extraction can be extrapolated to most any trainable context. A useful one I've worked with involved mapping supplier-partner relationships. A well built supply chain graph can identify every layer of risk in a single supply chain and provide the provenance to back it up.
IMO, the biggest blocker to more mainstream use of Knowledge Graphs (even in the commercial world) is an actually intuitive interface for knowledge exploration. The real market innovation behind GPT isn't its 175 billion parameters, its the prompt interface that makes ChatGPT so universally accessible.
- dberenstein1957 4y agoLovely, for me coreference resolution also is a huge issue. I created this package (https://github.com/Pandora-Intelligence/crosslingual-coreference https://github.com/Pandora-Intelligence/crosslingual-corefer...) which is currently the only viable solution to do coref resolution in Dutch and some other low-resource language. Also, if you want to scale, LLMs are going to prove to expensive, so eventually data need to be logged somewhere to create a fine-tuned model that can do sub-tasks and ideally do them better. What do you think?
- jeromechoo 4y agoNice! Yes, coreference resolution is surprisingly absent even in enterprise NLP. Depends on what you mean by "better". With more accuracy?