![]() ![]() Take a look at an example document/query: So let’s make this obsession a good one and put it to use in our problem!įirst, a brief detour: we’re going to be using the CNN/Daily Mail dataset in this project. Machine learning has emerged to be an extremely powerful technique in reading text and extracting important concepts from it it’s been the obsession of most computational linguists for the past few years. Why don’t we employ the power of machine learning to help us solve this problem? ![]() So, now what do we do? Let’s add a little…MACHINE LEARNING! With reading comprehension being so difficult, there’s no singular approach machines can take to solve the problem. One could spend a really long time looking for an answer in a piece of text and it could be right there staring at them on the page! For machines it’s even harder since language is highly flexible a sequence of words that you are looking for might not show up word-for-word in the passage. Second, it may not be obvious where to look in a passage for the answer to a question. ![]() Unlike a human, who is already familiar with how words mesh together in a sentence and is able to “understand” the true meaning behind one, a machine needs to somehow be taught that. ![]()
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December 2022
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