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I have the natural language sentences as follows:

This is a black chair. It is next to the table.

Each phrase that represents an object is annotated with an object Id. For example, in the above sentence, we have:

This: 15, black chair: 15, It: 15, table: 14

(where, 14 and 15 are object Ids)

I would like to train a model to predict the object Id of each phrase representing an object for a new sentence. From what I understand, each training example will consist of the following structure:

Inputs: sentence + object phrase

Output: object id (from 18 available ids)

I would need to repeat the above for each object phrase in a sentence

My question: How do I prepare the training data for this task? How do I represent each object phrase (eg: 'black chair') and each sentence for training the neural network?

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Nlp like spacy remove stop works and identify proper nouns and nouns. It does fairly well at identifying nouns but is not perfect. Try a sample of your data and see how accurate your percentages become. Use the medium model

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  • $\begingroup$ Post a paragraph and I will run it through some code and I will post back the results $\endgroup$ Feb 19 at 0:49
  • $\begingroup$ Thank you for your response. I'm able to extract noun chunks for the sentences. My question is how do I convert this data into vectors so that I can train a classifier? $\endgroup$
    – Sid
    Feb 19 at 1:17
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    $\begingroup$ Use count vectorizor or tfidf tokenizers $\endgroup$ Feb 19 at 4:57

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