A Guide to Natural Language Processing

Yeah, when you look at some of the SemEval contest winners or top 3, many use fairly simple methods combined into a powerful solution (except when LSTM with attention grabs the throne).

Couldn't agree more!

Recently I wrote a web-extension for Firefox that displays funny "Deep thought" quotes.

I wanted to analyse the quote text and fetch relevant images to animate in the background of the quote text. After reading several NLP tutorials, guess what I did as a first PoC - Pick the 3 longest words in a quote text and run an image search with those 3 words.

6 lines of plain javascript code that can be run anywhere almost instantly https://github.com/TheCodeArtist/deep-thought-tabs/blob/mast...

I get relevant images in the search results 99/100 times. The quirks of searching often result in the image adding to the funny-ness of the "Deep Thought" on display.

Its so effective that i ended up publishing the "Deep Thought Tabs" web-extension with this approach itself: https://addons.mozilla.org/en-US/android/addon/deep-thought-...

Later I tried using the nlp-compromise js library to identify "topics" of interest within a quote text - typically nouns, verbs, and adjectives. Comparing the results with my "3-longest-words" approach, I found that the longest words were anyways almost always the "topic" words that NLP identified for any given quote text.

You could use the same argument against pretty much any discipline that's undergoing active research. Of course no-one knows (yet) how to do it properly or else there would be no research going on. Image understanding, robotics, even non-computational disciplines such as medicine... Staying with the latter, take HIV for example: no-one knows how to heal it but I'm sure a lot of people are very grateful for the 80% solutions that prolong lives today.

So, in summary, you point is not wrong. But it's no reason for bashing computational linguistics. It is common across many disciplines to use not-yet-perfect solutions as long as you don't know how to do better.

That said, I don't fully agree with the notion that "hacking a solution" is the suggested way of doing things. Computational linguistics is a pretty wild field with a lot of sub-disciplines. In a lot of those, the state of the art consists of quite sophisticated approaches that are the result of years of research. Take speech recognition, for instance. Currently, deep learning approaches take the cake, but there is also a plethora of insights that have been gained from improving the traditional methods over decades.

I think, a more nuanced point of view is called for here.

Author profiling comes to mind as well

- Text generation and dialogue systems

> - Answering a question by returning a search result from a large body of texts. E.g. "How do I change the background color of a page in Javascript?" > - Answering a question, using a large body of facts. Like search, but now it gives a precise answer.

That is essentially a Natural Language Interface. There are simple ways to implement one for bots that receives simple commands[1]. The problem is that it quickly become very hard if you are trying to do something more open ended that a bot. So, there was simply no room to include it.

> - Improving the readability of a text. The article only mentions "understanding how difficult to read is a text".

The issue is that the formulas to measure the readability of a text cannot really be used to suggest improvements. That's because the user ends up focusing on improving the score instead of improving the text. To suggest improvements you need a much more sophisticate system.

> - Establishing relationships between entities in a body of text. E.g. we could build a fact-graph from sentences like "Burning coal increases CO2", and "CO2 increase induces global warming". Useful also in medical literature where there are millions of pathways.

This is one of the things that were axed, because in some sense it is simple if you just want to link together concepts without any causality, i.e. stuff that happens together. To do that you could link named entity recogniton (to find entities) and a simple way to find a relationship between words (i.e., they happen in the same phrase therefore they have related). However a more sophisticated form of the process, like the one that results in the Knowledge Graph[2] would be quite hard to do.

> - Finding and correcting spelling/grammatical errors.

That's a great idea, we will add how to detect spelling errors.

[1] https://medium.com/swlh/a-natural-language-user-interface-is...

[2] https://en.wikipedia.org/wiki/Knowledge_Graph

> - Establishing relationships between entities in a body of text. E.g. we could build a fact-graph from sentences like "Burning coal increases CO2", and "CO2 increase induces global warming". Useful also in medical literature where there are millions of pathways.

That's a simple example because with 'CO2' you at least have the same string that can serve as a keyword connecting those two facts. Usually in natural language we make frequent use of anaphora to refer to people, objects and concepts previously mentioned in the text by name.

Anaphora resolution is one of the really hard problems not only in NLP but in linguistics in general. The most simple anaphoric device in languages like English is pronouns and even with those it can be quite difficult to determine what a 'he' or 'she' refers to in context.

Thanks. Well, there are interesting things that we had to cut because they were too advanced for an introductory article. We were thinking about making a new article for them in a few months. And Natural Language Generation would be another great topic to talk about.

However, if you already have experience in the topic we would be happy if you would like to write a guest post for us.

gensim is one of the best libraries for word vectors and summarization. For parsing and NER, Stanford CoreNLP works best in my experience.

Have you heard of word mover’s distance? It works really well!

Like everything else, depends on your use-case. I have personally used TF-IDF vectors and token sets with Cosine and Jaccard distances in practice.

Some examples of use-cases: are you searching for "semantically similar", or "near duplicate"? You can compare documents under different metrics and different _representations_. Some representations are: LSA, PLSA, LDA, TF-IDF, and Set representations, along with metrics such as Jaccard Distance, Cosine Distance, Euclidean distance, etc.

Doc2vec is the Word2vec analog for documents.

Word Mover Distance on Word2Vec vectors.

There is an implementation in Textacy.

Let me try:

Take the famous example of [king] and [queen] being close neighbors in vector space after generating the word vectors ("embedding"). If you then use these vectors to represent the words in your text, a sentence about kings will also add information about the concept of queens, and vice versa. To a far lesser degree, such a sentence will also add to your knowledge of [ceo], and, further down, [mechanical engineer]. But it will not change the system's knowledge of [stereo].

The interesting thing about word2vec is that is an unsupervised method that build vectors to represent each word in a way that makes easy to find relationship between them.

There is a video by the creator of Gensim on word2vec and frieds: https://www.youtube.com/watch?v=wTp3P2UnTfQ

We didn't include it, simply because it relies on machine learning and we wanted to show simpler methods.

Yes, I agree that the applications for word vectors are not made as clearly as it should be. One direct application is as the first layer of a neural network [1], which could be part of either a 1-dimensional convolution or a recurrent neural network. Using pre-trained word vectors is a form of transfer learning and allows for much more predictive models with smaller amounts of training data.

[1] https://blog.keras.io/using-pre-trained-word-embeddings-in-a...

Essentially they are useful for comparing the semantic similarity of pieces of text. The text could be a word, phrase, sentence, paragraph, or document. One practical use case is semantic keyword search where the vectors can be used to automatically find a keyword's synonyms. Another is recommendation engines that recommend other documents based on semantic similarity.

Well there's word2vec, which while it isn't quite the same (its whole point is the vector classification it already embodies), I think is actually the kind of think you were asking for.

I worked with NLP for my research, and I used to build my corpora from wikipedia documents. Here's a tool that I've built to do it: https://github.com/joaoventura/WikiCorpusExtractor

Depends on what you want to try. NLTK has built in datasets. 20 Newsgroups is useful for trying lots of things.

There are a few different datasets that might be of use, depending on what you're playing with:-

- bAbI https://research.fb.com/downloads/babi/ and https://github.com/facebook/bAbI-tasks

- SQuAD https://rajpurkar.github.io/SQuAD-explorer/

- WebQuestions https://github.com/brmson/dataset-factoid-webquestions

Edit: there's also a great list of datasets on the ParlAI project page https://github.com/facebookresearch/ParlAI

Hmm, worked fine for me.

Thanks for your comment! Now, we have fixed the issue.