RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.
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A few questions regarding how to exploit RNNSharp #42
I highly appreciate your work and thank you very much for providing this model in C#. To add in a bit of context, I will be using your model during my research, and I would like to know as much as possible about how to exploit RNNSharp for NER. I would greatly appreciate if you could answer some of my doubts and questions that I haven't seen enough explained in the README or in other issues written by other users:
What is validated corpus in the process of encoding a model? How does it differ from the training corpus file?
Is there any quicker way of preparing training files in the format required for the encoding process? Say I would like to use a CONLL training corpus but, as the format is different, manually formatting requires a huge amount of time. How to go about?
How to do gazzeteer-matching? Say I have a list of named entities of locations from GeoNames and I want the algorithm to integrate them and do named entity matching in the decoding process. Is there any guide how to do so?
I would like to know how to feed features into the algorithm, such as handcrafted linguistic features (capitalization, linguistic pattern combinations and the like) to build a hybrid linguistic and deep-learning-based NER system.
How to perform evaluation metrics on our test dataset such as recall, precision and f1-score?
Thank you very much in advance, and have a nice day.
Hi,
I highly appreciate your work and thank you very much for providing this model in C#. To add in a bit of context, I will be using your model during my research, and I would like to know as much as possible about how to exploit RNNSharp for NER. I would greatly appreciate if you could answer some of my doubts and questions that I haven't seen enough explained in the README or in other issues written by other users:
What is validated corpus in the process of encoding a model? How does it differ from the training corpus file?
Is there any quicker way of preparing training files in the format required for the encoding process? Say I would like to use a CONLL training corpus but, as the format is different, manually formatting requires a huge amount of time. How to go about?
How to do gazzeteer-matching? Say I have a list of named entities of locations from GeoNames and I want the algorithm to integrate them and do named entity matching in the decoding process. Is there any guide how to do so?
I would like to know how to feed features into the algorithm, such as handcrafted linguistic features (capitalization, linguistic pattern combinations and the like) to build a hybrid linguistic and deep-learning-based NER system.
How to perform evaluation metrics on our test dataset such as recall, precision and f1-score?
Thank you very much in advance, and have a nice day.