title: NLP - which technique to use to classify labels of a paragraph?
body: <p>I'm fairly new to NLP and trying to learn the techniques that can help me get my job done. </p>
  
  <p>Here is my task: I have to classify stages of a drilling process based on text memos. </p>
  
  <p><a href="https://i.stack.imgur.com/j6BUS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/j6BUS.png" alt="enter image description here"></a></p>
  
  <p>I have to classify labels for "Activity", "Activity Detail", "Operation" based on what's written in "Com" column. </p>
  
  <p>I've been reading a lot of articles online and all the different kinds of techniques that I've read really confuses me. </p>
  
  <p>The buzz words that I'm trying to understand are</p>
  
  <ol>
  <li>Skip-gram (prediction based method, Word2Vec)</li>
  <li>TF-IDF (frequency based method)</li>
  <li>Co-Occurrence Matrix (frequency based method)</li>
  </ol>
  
  <p>I am given about ~40,000 rows of data (pretty small, I know), and I came across an article that says neural-net based models like Skip-gram might not be a good choice if I have small number of training data. So I was also looking into frequency based methods too. Overall, I am unsure which technique is the best for me.</p>
  
  <p>Here's what I understand:</p>
  
  <ol>
  <li>Skip-gram: technique used to represent words in a vector space. But I don't understand what to do next once I vectorized my corpus</li>
  <li>TF-IDF: tells how important each word is in each sentence. But I still don't know how it can be applied on my problem</li>
  <li>Co-Occurence Matrix: I don'y really understand what it is.</li>
  <li>All the three techniques are to numerically represent texts. But I am unsure what step I should take next to actually classify labels.</li>
  </ol>
  
  <p>What approach &amp; sequence of techniques should I use to tackle my problem? If there's any open source Jupyter notebook project, or link to an article (hopefully with codes) that did the similar job done, please share it here.</p>