<p>I don't know how useful this is but from my POV, word vector embeddings are naturally separated and the position in the sample space is closely related to different uses of the word. However like you said often a word may be used in several contexts.</p>

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<p>To Solve this purpose, generally encoding techniques that utilise the context like continuous bag of words, or continous skip gram models are used for classification of the usage of word in a particular context like change for either exchange or adjust. This very idea is applied in LSTM based architectures as well or RNNs where the context is preserved over input sequences.</p>

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<p>The interpretation of word-vectors isn't practical from a visualisation point of view, but only from 'relative distance' point of view with other words in the sample space. Another way is to maintain a matrix of the corpus with contextual uses being represented for the words in that matrix.
In fact there's a neural network that utilises bidirectional language model to first predict the upcoming word then at the end of the sentence goes back and tries to predict the previous word. It's called ELMo. You should go through the paper.<a href="https://arxiv.org/pdf/1802.05365.pdf" rel="nofollow noreferrer">ELMo Paper</a> and this <a href="https://blogs.nvidia.com/blog/2018/08/27/nlp-deep-learning/" rel="nofollow noreferrer">blog</a></p>

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<p>Naturally the model learns from representative examples. So the better training set you give with the diverse uses of the same word, the better model can learn to utilise context to attach meaning to the word. Often this is what people use to solve their specific cases by using domain centric training data.</p>

<p>I think these could be helpful:
<a href="https://arxiv.org/pdf/1301.3781.pdf" rel="nofollow noreferrer">Efficient Estimation of Word Representations in
Vector Space</a> </p>
Natural Language Processing techniques for understanding contextual words
<p>Take the following sentence:</p>

<pre><code>I'm going to change the light bulb
</code></pre>

<p>The meaning of <code>change</code> means <code>replace</code>, as in someone is going to replace the light bulb. This could easily be solved by using a dictionary api or something similar. However, the following sentences</p>

<pre><code>I need to go the bank to change some currency

You need to change your screen brightness
</code></pre>

<p>The first sentence does not mean <code>replace</code> anymore, it means <code>Exchange</code>and the second sentence, <code>change</code> means <code>adjust</code>.</p>

<p>If you were trying to understand the meaning of <code>change</code> in this situation, what techniques would someone use to extract the correct definition based off of the context of the sentence? What is what I'm trying to do called?</p>

<p>Keep in mind, the input would only be one sentence. So something like:</p>

<pre><code>Screen brightness is typically too bright on most peoples computers.
People need to change the brightness to have healthier eyes.
</code></pre>

<p>Is not what I'm trying to solve, because you can use the previous sentence to set the context. Also this would be for lots of different words, not just the word <code>change</code>.</p>

<p>Appreciate the suggestions.</p>

<p><strong>Edit:</strong> I'm aware that various embedding models can help gain insight on this problem. If this is your answer, how do you interpret the word embedding that is returned? These arrays can be upwards of 500+ in length which isn't practical to interpret. </p>