The task of summarization is a classic one and has been studied from different perspectives. The task consists of picking a subset of a text so that the information disseminated by the subset is as close to the original text as possible. The subset, named the summary, should be human readable. The task is not about picking the most common words or entities. Think of it as a quick digest for a news article.
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If you come from a statistical background or a machine learning one then probably you don’t need any reasons for why it’s useful to build language models. If not, here’s what language models are and why they are useful.
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One of the reasons why it’s so hard to learn, practice and experiment with Natural Language Processing is due to the lack of available corpora. Building a gold standard corpus is seriously hard work. That’s why resources are so scarce or cost a lot of money. In this post, I’m going to aggregate some cool resources, some very well known, some a bit under the radar.
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NLTK (Natural Language ToolKit) is the most popular Python framework for working with human language. There’s a bit of controversy around the question whether NLTK is appropriate or not for production environments. Here’s my take on the matter:
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If I ask you “Do you remember the article about electrons in NY Times?” there’s a better chance you will remember it than if I asked you “Do you remember the article about electrons in the Physics books?”. Here’s why: an article about electrons in NY Times is far less common than in a collection of physics books. It is less likely to stumble upon the “electron” concept in NY Times than in a physics book.
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Throughout this blog, we seek to obtain good performance on our classification tasks. Classification is one of the most popular tasks in Machine Learning. Be sure you understand what classification is before going through this tutorial. You can check this Introduction to Machine Learning, specially created for hackers.
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Have you ever noticed what happens when you hear a name you haven’t heard before? You automatically put it in a bucket, the girl names bucket or the boy names bucket. In this tutorial, we’re getting started with machine learning. We’ll be building a classifier able to distinguish between boy and girl names. If this sounds interesting read along. If you expect a tonne of intricate math, read along. It’s easier and more fun than you think.
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Few people realise how tricky splitting text into sentences can be. Most of the NLP frameworks out there already have English models created for this task.
You might encounter issues with the pretrained models if:
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This is probably the first post I should have written on the blog. The thing is, I did machine learning and natural language processing for a long time before putting the concepts in order inside my own mind.
I’ve learned techniques and hacks to boost precision of classifiers before fully understanding how a classifier computes its weights or whatever. So I guess it makes sense to publish a general introductory post after some real hands-on posts.
Here’s a popular diagram used to describe what data science usually implies:
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The most direct definition of the task is: “Does a text express a positive or negative sentiment?”. Usually, we assign a polarity value to a text. This value is usually in the [-1, 1] interval, 1 being very positive, -1 very negative.
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