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BERT-ML Architecture perform sequential tagging for mention detection, we have decided to solve the problem with the following two steps:
Source publication
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforc...
Contexts in source publication
Context 1
... a sentence {w 1 , w 2 , ....w n }, we feed it to the BERT model to obtain contextual BERT embeddings for each word as {v 1 , v 2 , ...v n }, capturing each word's context via many attention heads in each of its layer. These embeddings are then fed to a linear feed forward layer to obtain labels {y 1 , y 2 , ...y n } corresponding to each each word (see Figure 1). This entire network is trained with each epoch thereby fine-tuning the BERT embeddings for the NER task. ...
Context 2
... prediction is made for X tokens during training and testing. Figure 1 shows both the architecture of the proposed model, and the NER annotation style. ...
Context 3
... a sentence {w 1 , w 2 , ....w n }, we feed it to the BERT model to obtain contextual BERT embeddings for each word as {v 1 , v 2 , ...v n }, capturing each word's context via many attention heads in each of its layer. These embeddings are then fed to a linear feed forward layer to obtain labels {y 1 , y 2 , ...y n } corresponding to each each word (see Figure 1). This entire network is trained with each epoch thereby fine-tuning the BERT embeddings for the NER task. ...
