As part of the progression of machine learning components with increasing levels of sophistication, implement version 2 ("dsmvp-v2") with the following characteristics:
A machine learning module that can learn to detect racially biased expressions in context based on input labeled data of <context, expression, classification> triples.
A possible implementation can make use of contextual text classifiers, such as those based on RNN = Recurrent Neural Networks such as the LTST architecture (Reference: https://spacy.io/usage/examples#textcat).
RNN allows the classifier to be "sequentially contextual", i.e. to classify a given phrase or expression dependent on the context in which it is used.
Coding of dsmvp-v2 should be similar to and share many aspects of how dsmvp-v1 in the repository is implemented, using Jupyter notebook and accessing the database via webapi, etc.
As part of the progression of machine learning components with increasing levels of sophistication, implement version 2 ("dsmvp-v2") with the following characteristics:
A machine learning module that can learn to detect racially biased expressions in context based on input labeled data of <context, expression, classification> triples.
A possible implementation can make use of contextual text classifiers, such as those based on RNN = Recurrent Neural Networks such as the LTST architecture (Reference: https://spacy.io/usage/examples#textcat).
RNN allows the classifier to be "sequentially contextual", i.e. to classify a given phrase or expression dependent on the context in which it is used.
Coding of dsmvp-v2 should be similar to and share many aspects of how dsmvp-v1 in the repository is implemented, using Jupyter notebook and accessing the database via webapi, etc.