A Hybrid Graph Model for Distant Supervision Relation Extraction | Springer for Research & Development (ESWC 2019)(About) > Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. >... we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise.
Introducing Metadata Enhanced ULMFiT | Novetta Nexus(About) > Our first idea was to combine a structured data model with the text model from fast.ai. Later, when thinking about Jeremy Howard’s “Introduction of Language Modeling”2 in the 2018 course, we remembered his example of generating technical abstracts for papers. He had special flags that indicated the two sections of the abstract, , which indicated the category and , which was the text of the abstract. We realized that you might be able to pass the model information in a similar fashion
[1905.05950] BERT Rediscovers the Classical NLP Pipeline (2019)(About) > We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.