Can we use our own data set to train the models and predict our own test set?

[xinxu1018]

Can we use our own data set to train the models and predict our own test set?

[Sshanu]

You can, just make sure the input format remains the same.

On Wed, Oct 17, 2018 at 7:24 AM xinxu1018 notifications@github.com wrote:

Can we use our own data set to train the models and predict our own test set?

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[xinxu1018]

@Sshanu Thanks so much for your quick response! Please allow me to ask one more question. Since I am using word embeddings trained over my specific corpus instead of your given Glove embeddings, how can I get my embeddings in the same format with the Glove embedding file you provided in the data folder and use it in your designed LSTM model?

All the best!

[Sshanu]

I first extracted words and stored it in a list named as vocab, then extracted word embedding and stored it in a numpy array. If 2nd word in vocab is "the", the 2nd row in numpy array will have word embedding corresponding to "the". I then saved both vocab and word embedding numpy array using pickle. So, you can create a similar array and vocab, or you can change the code to load embeddings.

On Wed, Oct 17, 2018 at 9:14 PM xinxu1018 notifications@github.com wrote:

@Sshanu https://github.com/Sshanu Thanks so much for your quick response! Please allow me to ask one more question. Since I am using word embeddings trained over my specific corpus instead of your given Glove embeddings, how can I get my embeddings in the same format with the Glove embedding file you provided in the data folder and use it in your designed LSTM model?

All the best!

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[xinxu1018]

@xinxu1018 That is so informative! Thanks a lot!

[xinxu1018]

@Sshanu It works! Thanks a lot! Can I ask a follow-up question again? If I wanna classify relations between multi-word terms (in your case it is one-word term pairs), how can I preprocess the sentences before I go to the step of dependency path extraction? Do you have any suggestions? One way I am considering is to connect every word within a multi-word term using underscores (like, "system configuration" to "system_configuration" ) and then treat them as a one-word term. Then follow your designed procedures. Not sure if it will work. Do you have any ideas?

Many thanks!

[Sshanu]

You can try this approach but its shortcoming is that you don't have word embedding for the multi-word term. Create a dependency tree, then choose entity from the two word which is below another one in the tree, then the information regarding the other word will be computed from the lstm -tree, and instead of only using features of lca, entity1, and entity2 from the lstm-tree for relation classification, also use the features of the other word from the lstm-tree.

I did not work on Relation Classification or any related field after this project, this project was my first in NLP, that's why I have very less knowledge in this relation classification or extraction.

On Thu, Oct 18, 2018 at 12:57 AM xinxu1018 notifications@github.com wrote:

@Sshanu https://github.com/Sshanu It works! Thanks a lot! Can I ask a follow-up question again? If I wanna classify relations between multi-word terms (in your case it is one-word term pairs), how can I preprocess the sentences before I go to the step of dependency path extraction? Do you have any suggestions? One way I am considering is to connect every word within a multi-word term using underscores (like, "system configuration" to "system_configuration" ) and then treat them as a one-word term. Then follow your designed procedures. Not sure if it will work. Do you have any ideas?

Many thanks!

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[xinxu1018]

@Sshanu Thanks a lot! Hope you everything goes very well!

[xinxu1018]

@Sshanu Could you please provide your word_embd_wiki file? I cannot find the embedding file in your given data folder. Thanks for you help!

Best,

[Sshanu]

My google drive is full, please share a folder with me, where I will upload the word_embed file.

On Thu, Oct 18, 2018 at 9:03 PM xinxu1018 notifications@github.com wrote:

@Sshanu https://github.com/Sshanu Could you please provide your word_embd_wiki file? I cannot find the embedding file in your given data folder. Thanks for you help!

Best,

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[xinxu1018]

@Sshanu How can I share a folder with you? What's your address? Sorry, I am new here!

[xinxu1018]

@Sshanu Hi Sshanu, I just shared a google drive folder to the email you provided in your Github profile. Not sure am I doing right! Many thanks!

[Sshanu]

Oh, please share it with sshanukr@gmail.com

On Thu, Oct 18, 2018 at 9:43 PM xinxu1018 notifications@github.com wrote:

@Sshanu https://github.com/Sshanu Hi Sshanu, I just shared a google drive folder to the email you provided in your Github profile. Not sure am I doing right! Many thanks!

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[xinxu1018]

@Sshanu I have shared to your gmail. Please check and many thanks!

[xinxu1018]

@Sshanu Hi Sshanu, got your shared file! You helped me a lot! I am just wondering do you have the original code that was used to separate embedding file into vocab and word_embedding arrays? Then I can generate my own trained embeddings into the format aligned with your designed method. Could you please share me the code? Thanks again!

[Sshanu]

I don't have the file, if you are having a problem in generating the exact file, then simply try storing the embeddings in numpy array and vocabulary in a list, my code will work afterward.

On Fri, Oct 19, 2018 at 10:17 AM xinxu1018 notifications@github.com wrote:

@Sshanu https://github.com/Sshanu Hi Sshanu, got your shared file! You helped me a lot! I am just wondering do you have the original code that was used to separate embedding file into vocab and word_embedding arrays? Then I can generate my own trained embeddings into the format aligned with your designed method. Could you please share me the code? Thanks again!

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