How to Get into Natural Language Processing

https://blog.ycombinator.com/how-to-get-into-natural-language-processing/

by craigcannon • 10 years ago

340 75 10 years ago

blog.ycombinator.com

demonshalo5• 10 years ago

How? just get started working on a fun problem. A good place to start is keyword extraction. You don't need a PhD or expensive tools. All you need is some free time and willingness to read some cool stuff.

Copy a few articles into text files and get working on implementing some of these methods until you have enough of an understanding to construct your own methods for the fun of it.

Here's some good reading material:

https://www.facebook.com/notes/facebook-engineering/under-th...

https://www.researchgate.net/profile/Stuart_Rose/publication...

http://cdn.intechopen.com/pdfs/5338.pdf

https://arxiv.org/pdf/1603.03827v1.pdf

https://www.quora.com/Sentiment-Analysis-What-are-the-good-w...

http://hrcak.srce.hr/file/207669

http://nlp.stanford.edu/fsnlp/promo/colloc.pdf

https://arxiv.org/ftp/cs/papers/0410/0410062.pdf

http://delivery.acm.org/10.1145/1120000/1119383/p216-hulth.p...

Edit: Don't get deterred by the math formulas in these papers. They look far more complicated than they actually are.

garysieling• 10 years ago

Another fun thing is to paste article text into some API, like the Watson demo, so you can see what kinds of things are possible:

https://alchemy-language-demo.mybluemix.net/

I played around with this a bit to develop https://www.findlectures.com, so knowing what works/doesn't work there I'm developing some NLP scripts to support my use cases.

demonshalo5• 10 years ago

I never thought about this particular use-case. The subtitle for TED talks should be an ocean of info for you to extract keywords from :D Pretty neat site you got there. I will be using it. Thanks!

bosie• 10 years ago

Thank you very much for the reading material

demonshalo5• 10 years ago

u welcome!

jventura• 10 years ago

I would say that a good example for starting in this field would be to implement something like Tf-Idf [0] for identifying keywords on a set of documents. I don't know where one can find current datasets for this, but I made WikiCorpusExtractor [1] to build sets of documents from the Wikipedia.

The only thing one really needs is to count the frequency of words in each document and do very simple math. Tf-Idf is still very relevant today and provides you with a very good idea on how statistics is used on text-mining.

[0] https://en.wikipedia.org/wiki/Tf%E2%80%93idf

[1] https://github.com/joaoventura/WikiCorpusExtractor

demonshalo5• 10 years ago

I started even simpler than that. I started by just eliminating stopwords and count the frequency in each word in the document itself. I did not use a set of documents as the goal was for the algorithm to be used on the spot for a single block of text.

A few months later and after many iterations + a whole lot of testing, the algorithm now can extract super relevant keywords 90%+ of the time!

I wish I knew about the WikiCorpusExtractor. Thanks for the link!

beders• 10 years ago

Fun problem: Write a parser for the English language. See it fail at tweets :)

divbit• 10 years ago

I dont to need to write a program to do that.

_spoonman• 10 years ago

I think "Paths" is a terrific idea. There have been times where I've wanted to do a "first principles" look at a topic but don't want to go back through my HN upvotes. "Paths" allows for a curated and practical advice-driven jumping off point. Looking forward to more content. Best of luck with it!

vincentschen3• 10 years ago

So glad that you enjoyed the post! What are other topics that you've wanted to hear about?

deegles• 10 years ago

There are a ton of libraries and tools available for NLP, so I feel that side is relatively mature.

What I want are more tools for Natural Language Generation. Can anyone recommend some good ones? (beyond what's on Wikipedia)

mwexler• 10 years ago

+ this. It's actually much harder than folks think; most solutions are still rules and sentence fragments that you string append. Automated Insights and Narrative Science are both doing interesting work, but more is needed here.

(...though I don't agree that NLP is mature, just more mature than this. Lots of room to improve NLP as well!)

will_pseudonym4• 10 years ago

I'm not sure what methods they use, but the "single sentence reply suggestions" created by Google's Inbox are the highest quality natural language generation that I have come across.

tyingq3• 10 years ago

I believe this is the paper that describes the approach they are using: http://www.kdd.org/kdd2016/papers/files/Paper_1069.pdf

Looks less like NLG, and more like picking existing responses from a (probably huge) corpus using ML. Hard to replicate unless you have access to the kind of data Google has.

tyingq3• 10 years ago

I tried playing around with NLG a couple of years ago. The only tool I was able to get up and running well enough to do anything was SimpleNLG[1]. It's java, not my forte, but was straightforward otherwise. Here's a basic example of use: https://github.com/simplenlg/simplenlg/wiki/Section%20V%20%E...

[1]https://github.com/simplenlg/simplenlg

earthly10x• 10 years ago

One of the best places to start is reading this patent from Berkeley Lab/DOE which word2vec was based on https://www.google.com/patents/US7987191

AlexCoventry• 10 years ago

What insight does the patent offer, which you won't get from reading the word2vec papers?

p1esk2• 10 years ago

NLP right now looks like the computer vision 5 years ago: DL methods are starting to work really well, so a lot of "traditional" methods to process text might soon become obsolete.

The goal is to just feed gigabytes of raw text to a huge, complex neural network, and hope it will extract relevant features.

demonshalo5• 10 years ago

The problem is datasets. How can you distinguish a good result from a bad result? In some cases, depending on the user, it could be both at the same time.

Most advancements in ML is not accomplished by some new super algorithm. Rather, advancements are reached when new datasets are presented!

d_burfoot2• 10 years ago

Why is NLP Hard? ... Language is highly ambiguous - it relies on subtle cues and contexts to convey meaning.

This is true, but it is only part of the answer.

Another part of the answer is what I call the Long Tail of Grammar. It turns out that if you try to write down all the rules of grammar, you will not get 40 or 60 rules, but something more like 100s or maybe even 1000s of rules. Most of those rules are obscure, rare, archaic, or useable only in specific contexts or with specific words. However, they are part of the language, a native speaker will be able to use them and comprehend them without difficulty, and an NLP system must be able to "understand" them in order to extract the correct meaning from a sentence.

As just a minor example off the top of my head, compare the phrase "peeled peach" with "hairy-peeled peach". The former phrase means a peach without a peel, while the latter means a peach with a hairy peel. So a good NLP system must not only recognize the existence of the two grammatical rules, but also be able to disambiguate them correctly.

will_pseudonym4• 10 years ago

Not just that, grammar rules can come into existence/change spontaneously. Here's an example and name for this phenomenon:

Because syntactic change.

WildUtah• 10 years ago

That isn't really a rule change. The lexicon just added a new preposition, 'because,' to the old list. It means almost the same thing as the old preposition 'because of.'

There's a difference between coining a new word and changing a grammar rule.

chch2• 10 years ago

or useable only in specific contexts or with specific words.

A good example of this is the Winograd Schema. You might think you can figure out a good algorithm for anaphoric resolution (i.e. If you see "Sally called and she said hello.", who is "she"?) that just relies on the structure of a sentence, without considering semantics.

But here's a counterexample:

"The city councilmen refused the demonstrators a permit because they feared violence."

Who are 'they'?

"The city councilmen refused the demonstrators a permit because they advocated violence."

Now who are 'they'?

If you're like most people, even though only the verb changed, the binding of 'they' based on the deeper semantic meaning also changed.

These sentences are called Winograd Schema[1], and there are plenty more like it.

[1] https://en.wikipedia.org/wiki/Winograd_Schema_Challenge

ice109• 10 years ago

re the council people sentences: I don't understand the problem. they're ill-defined sentences. we use heuristics to parse them but those heuristics can fail (the council denied the demonstrators permit because they feared violence... and the council was obliging). just teach the computer the heuristics like we learn them.

impostervt• 10 years ago

Just wanted to say thank for a great and interesting comment. Never seen the complexity of NLP summed up so well.

czep• 10 years ago

This is a recognized problem and is called Word Sense Disambiguation. It's hard but not intractable. One issue is that such sentences are themselves ambiguous and even human readers may disagree on their meaning. A statistical system can make a guess based on a large corpus of word-context pairs, which can approximate what a human does when attempting to disambiguate the meaning. It won't be perfect, but again, part of this is due to the fact that the sentence as it stands alone is insufficient.

Presumably, such sentences would be contained in a paragraph that would provide additional clues as to whom the word 'they' refers. Given additional context, you could then ask, "were the protesters or the councilmen fearing violence?" Document summarization and fact extraction systems could then approximate humans in such a task.

What's interesting is that word sense ambiguity underlies a lot of comedy. For instance, "Time flies like an arrow; fruit flies like a banana." The close juxtaposition of the word "like" being used in two different contexts is what makes this sentence "funny". I think it's not too far off to say that we could eventually teach AI systems to recognize humor.

leereeves• 10 years ago

compare the phrase "peeled peach" with "hairy-peeled peach"

Is that a rule of grammar, or simply the meanings of the adjectives "peeled" and "hairy-peeled"?

andrewtbham3• 10 years ago

yeh it seems more like another example of word sense disambiguation.

d_burfoot2• 10 years ago

In my mind there are two very distinct patterns:

- Xed Y = a Y that has been Xed, where X is a verb

- Xed Y = a Y that is equipped with an X, where X is a noun.

For the latter pattern, consider examples such as "red-lipped woman", "rosy-fingered dawn", "sharp-toothed pike", "horned owl", etc.

argonaut• 10 years ago

Your example isn't even a specific grammar rule. It's a very broad language rule, which is: "descriptions should conform to the real world."

Someone who has never seen a peach might assume that hairy peeled peach refers to a hairy peach without a peel. If you've seen a peach before, this makes no sense. So the assumption is that the peach has a hairy peel.

This is why purely statistical / supervised learning-based NLP alone is not enough.

throwaway91111• 10 years ago

This seems like a great argument for automatic grammar learning. How far has current research taken us in that direction?

I'd much rather feed it edge cases to accommodate than rules to follow.

hiddencost3• 10 years ago

They tried that in the 70s and 80s until they realized they were wasting their time. Probabilistic context free grammars were a thing, also.

Long story short, there's no such thing as grammar but they're a nice fiction for talking about communication. The deeper you get into NLP the more you (1) see what jelinek was talking about when he said "every time I fire a linguist, accuracy goes up" (he was hiring physicists and information theorists), and (2) realize that basically every thought, belief, and statement is deeply ambiguous, and that most human communication is ad hoc.

Also, the more time you spend looking at live data from users, the more you realize that the notion of language as a generally shared system of meaning is not real. Trivial communication about basic tasks is doable, although you often will fuck up there too when talking to someone with deeply different cultural expectations.

If you think that _any_ sentence has unambiguous meaning, you should try to meet some people who are more different than you are. Or get into deeper conversations with the people you know.

Edit: typo

blcArmadillo• 10 years ago

I think this is a good idea for a series. Although I think more detail needs to be given on the actual path, that is after all the purpose of the series. Most of this article seemed to be describing what NLP is and why it's hard. This isn't bad and some attention should be given to it but people looking to find the path into NLP will already be familiar with most of this information. I was expecting a bit more of a syllabus type format. There was mention of needing some college level algebra and statics, I would have liked more detail in this area with links to more resources (classes, articles, datasets, etc). Keep up the good work!

mswen• 10 years ago

Agreed on more substantive detail needed. I was surprised at the lack of mention of many of the basic techniques and domains that a person interested in should consider learning about.

The following are all germane but not mentioned: text analysis/mining, controlled vocabularies, indexing, taxonomies, ontology, semantic web, latent semantic analysis, latent dirichlet allocation, corpus analysis, document similarity analysis, tf-idf, ngrams, and skip grams just to mention a few.

In general the article is a good idea but their needs to be more of a description of the domain landscape and then "paths" plotted through that landscape that lead to interesting and useful competency.

vincentschen3• 10 years ago

That's a great point. I wonder if there would be a better way to introduce meaningful, actionable topics of study to an introductory-level audience of people who may have never heard of NLP.

fnl2• 10 years ago

text summarization are examples of NLP in real-world products

Can someone point me to a satisfying demo of a professional text summarization software?

whodunser• 10 years ago

The autotldr bot on reddit gets a lot of praise:

http://smmry.com (demo here)

https://np.reddit.com/r/autotldr/comments/31bfht/theory_auto...

rsivapr• 10 years ago

Worth mentioning here, the text summarization engines are pretty much only usable on news articles or text with clean paragraph structure (yes, non-tech literature works too). Popular text summarization tools as they stand now, fails on anything else.

hobofan2• 10 years ago

I like the idea of the Paths series, though some of the points in this first article read like they could be written about most "emerging technologies". Anyway, I'm looking forward to the next one!

The two questions about the PhD's do feel a little bit misplaced for a startup audience. Who here stops and thinks "Am I supposed to have a PhD to do that?", when setting out to start something new? (<insert theranos reference here>)

fauigerzigerk2• 10 years ago

I think a better question would be: How much math do I need to approach NLP in a way that enables me to be among the best?

PhD is just an academic title and as such it is neither a necessary nor a sufficient prerequisite to approach NLP from a mathematical angle.

hobofan2• 10 years ago

How much math do I need to approach NLP in a way that enables me to be among the best?

The best in what? If you mean pushing the boundaries of research, yes, then your path there will likely involve a PhD. If you mean building the best technology products, then being able to read, understand and implement the biggest recent advances is enough and usually requires far less mathematical knowledge.

(That was intentionally written generically since I think that it applies to more than just NLP)

andrewtbham3• 10 years ago

I researched deep learning for nlp for a year and compiled this list of papers and articles about some of the most interesting topics.

https://github.com/andrewt3000/DL4NLP/blob/master/README.md

p1esk2• 10 years ago

Have you built anything interesting?

andrewtbham3• 10 years ago

I did some kaggle contests using image stuff.

With regards to nlp, I have a site that is using a spider to collect headlines for stocks and I have been working on clustering, sentiment analysis, and text summarization. But it's I haven't completed it.

http://www.teslanewsfeed.com/

danso• 10 years ago

Love the concept of this "How to" series. Seems like it'd be a good opportunity to spotlight the interesting HN threads on any given topic.

e.g. for NLP:

- https://news.ycombinator.com/item?id=11686029

- https://news.ycombinator.com/item?id=11690212

- https://news.ycombinator.com/item?id=1839611

rstuart• 10 years ago

If you are interested in working in NLP, feel free to reach out to Kapiche. The website, Twitter or hello at kapiche dot com are all good options.

elchief• 10 years ago

http://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf is fun and easy. Text analysis with bash

JoeDaDude• 10 years ago

I'm a little surprised GATE [1], the General Architecture for Text Engineering tool is not mentioned. It is incredibly flexible, open source and has a very long track record as a research and prototyping tool.

[1] https://gate.ac.uk/

ktRolster4• 10 years ago

This book was really helpful for me when I was getting started with natural language processing: http://www.nltk.org/book/

It's practical, readable, and it's free.

visarga2• 10 years ago

If you want to play with NLP, then just try Gensim, sklearn and Keras. If you're serious about NLP, it's hard stuff, you need a PHD in the field.

In a way, vision is easier. Instead of discrete symbols (words) it's continuous signal which are much easier to interpret and generate from neural networks. By comparison, best language models are behind best image generation models (2-3 years behind, in my estimation).

For example, there are few applications of GANs to text, and many applications to images, GANs being the hottest thing in deep learning now. So you have to keep in mind that NLP is by and large still not solved. There is no decent conversational chat bot yet. We can reason over small pieces of text but that is far from full understanding. NLP at this level is hard.

What you can easily do now is to classify text, detect sentiment, entities, word vectors, grammatical parsing and summarization. All are low level stuff.

amelius• 10 years ago

So what you are saying is that a computer would be able to understand sign-language more easily than spoken language?

visarga2• 10 years ago

Computers can transcribe spoken language into text at great accuracy, but they can't understand the meaning of text at the same level of accuracy yet. Meaning is much harder than simple transcription. Voice recognition is to speech like OCR to print. What we want is to speak to computers and have them understand what we mean, like humans. Such an AI would be able to carry a conversation, extract data from and reason over documents, or perform complex actions based on verbal commands. They would need to have a good physical and conceptual understanding of the world, otherwise they could not use reasoning.

mankash666• 10 years ago

Just noting that the article is better titled "why to get into ..."

edblarney• 10 years ago

I strongly recommend Stanford's youtube min-course by Dan Jurafsky & Chris Manning

https://www.youtube.com/watch?v=nfoudtpBV68

posterboy4• 10 years ago

Take this simple example: “I love flying planes.”
Do I enjoy participating in the act of piloting an aircraft? Or am I expressing an appreciation for man-made vehicles engaged in movement through the air on wings

Clearly the latter, as the former begets the infinitive, "I love to fly ...".

Maybe I am wrong, going by the American usage of the gerund I clearly am, but then "I want going flying" sounds ridiculous in any case. Maybe I am missing the difference, so as a second language speaker, I'd love to be corrected.

posterboy4• 10 years ago

note to self: I read, it might not be the gerund in this case, but the past participle.

How to Get into Natural Language Processing

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