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In our AutoNER model, these “unknown” positions have undefined boundary and type losses, be- cause (1) they make the boundary labels unclear; and (2) they have no type labels. Therefore, they are skipped.
Is that mean high quality phrase should not have entity types that we are trying to identify? Otherwise, the model will predict it as Entity Type: None as shown in Figure 2 for 8GB RAM. And if AutoNER is applied to the example of Figure 1, can it and should it identify prostaglandin synthesis as a named entity?
Thanks.
The text was updated successfully, but these errors were encountered:
aspect term extraction, which can be viewed as an entity recognition task of a single type for business reviews. As shown in our experiments, our models can outperform Distant-LSTMCRF significantly on the laptop review dataset.
Because laptop review dataset has no entity type for entity mentions, I think there is only a single type for the latop review dataset, ComputerTerm or not. That's why they can be compared with the Aspect Term extraction model
Hi, the original paper says
Is that mean high quality phrase should not have entity types that we are trying to identify? Otherwise, the model will predict it as
Entity Type: None
as shown in Figure 2 for8GB RAM
. And if AutoNER is applied to the example of Figure 1, can it and should it identifyprostaglandin synthesis
as a named entity?Thanks.
The text was updated successfully, but these errors were encountered: