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Comment by paulsutter | Hacker News Reader

NLP is one of the most challenging areas of research, and nothing in this article will help solve even 0.009% of those challenges

Example of the wisdom herein:

> Remove words that are not relevant, such as “@” twitter mentions or urls

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Wow. That's like saying a person's name isn't important. That's like saying Latin words aren't relevant or Spanish words aren't relevant for processing English. To explain why this is bad since if the author is confused(see edit), others may be as well: Twitter mentions are contextual data. They're either addressing someone by name or they're being used in a cheeky way to replace another word.

I could quite easily say "I'm going to @chicago this weekend to see @taylor_swift". Now see if this sentence makes any sense: "I'm going to this weekend to see". Nope. What you need to do is translate them, like any other word. Sure it's not an English word, but you wouldn't ignore the word "hola" just because it's not an English word. Now, if your NLP application doesn't rely on this data, sure, throw it away. But if you're looking at Twitter and throwing away any mentions, you're not really processing natural language, are you?

Sure, it's really hard to translate. Am I talking about Chicago the city or Chicago the band? Maybe Chicago the movie?

Well that's why NLP is one of the most challenging areas of research, and nothing in this article will help solve even 0.009% of those challenges.

(edit) Maybe confused isn't the right word... they may just have other priorities for their use case. However, that doesn't excuse the title.

The approach I've seen to handling Twitter mentions is to replace all mentions with a "<userid>" token, which handles that potential loss of context, although not perfectly.

>Now, if your NLP application doesn't rely on this data, sure, throw it away. But if you're looking at Twitter and throwing away any mentions, you're not really processing natural language, are you?

The data is natural language, and your system is processing it. NLP doesn't mean "using computation to model exactly and completely the entire human language faculty". NLP is distinct from text processing in that often text files may contain forms of unstructured information other than natural language. The data being processed here is natural language.

This comment is funny, and also unfortunate. Overall, the article gives a broad overview of a typical NLP pipeline, and demonstrates the concepts with a neat example. Sure it could be improved, but it seems that you interpreted the fact that it can be improved as a sign that it's almost entirely unhelpful. In what world is that mindset useful?

For the example task in the article (classifying whether a tweet is about disasters), it would be genuinely surprising if `@` mentions were meaningful. Sure, this would be something you would investigate, but the general idea of `removing words that are not relevant` as a pre-processing step is definitely not bad advice.

Hi,

Author here, happy to answer questions and share our vision of it. Many problems require more complex approaches, and we definitely have Fellows tackle some of those (https://blog.insightdatascience.com/entity2vec-dad368c5b830).

That being said, when it comes to the volume of practical applications that come up for the many teams that we work with, the vast majority can be solved by the techniques outlined in the post. These techniques are simpler, but often overlooked. We believe that they should often be a starting point, and most of the time they end up being good enough for the job.

A better title for your article might be “How to solve most text-processing problems”, because you’re really not talking about NLP.

I'd agree - and also, it should be made clear that a Twitter classifier with 80% accuracy is probably a long way from something that you would apply to the actual Twitter firehose.

But the article is a very nice introduction to text mining (alas, not NLP)!

Sarcasm can be fun, but it would seem odd to leave such a thing off a list of basic things to do to clean your data.

Large amounts of text based problems that companies actually have problems I think can be solved with approaches in this article. I'd be surprised actually if many need to go all the way through to the end, which gets up to word vectors and convolutional neural networks.

My thought is exactly. While the content is not bad, the title is a clickbait. It is just how people deal with text classification.

Yes that title isn't just pedantically incorrect... its extremely disingenuous.

Where did the 90% even come from?

Every point in the list of data cleaning tips removes contextual information that may actually help models learn. I'd be careful with applying this advice blindly.

I took this article more at a level of DS online coursework as opposed to useable in industry.

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