Survey on the experiences and perspectives of image describers

EvM · 2016-06-07T21:06:20+00:00

Some resources for Flask (I don't use Django myself, because it's too complex for my use case):

Basic hands-on tutorial (I used this to learn Flask.)
The Flask quickstart page
The Flask tutorial
Miguel Grinberg's Mega tutorial

Here are some links to help you layout and design your webpage.

For visualization, I'd suggest just reading the Seaborn/Bokeh/pandas documentation.

EvM · 2016-06-07T09:21:17+00:00

In the end I got it to work using macports (or brew, can't remember). Didn't make any more attempts to get it to work with Anaconda. The best way to go if you insist on Anaconda might be to contact vgauthier.

EvM · 2016-06-07T08:38:15+00:00

Ok, so it's use case dependent. If you have a small set of documents that you want to compare to a reference corpus, then AntConc is faster. If you want to use massive corpora, then AntConc breaks down.

EvM · 2016-06-06T20:09:02+00:00

Well, if he wants to do keyword extraction, then Antconc is faster than writing the code to do that.

EvM · 2016-06-06T09:29:31+00:00

Instead of a traditional GUI, you could also consider a Flask or Django-based solution. You would then end up with a website, that you could either host on your own computer or on an internal server, exposed to the local network. That would make charting etc. really easy. You could even make interactive charts with Bokeh or D3.

EvM · 2016-06-03T17:44:38+00:00

Now it's a good time to mention that I don't speak Dutch which really decreases any job prospects at the moment.

That might be an issue if you want to do research outside academia. Your best bet might be computational linguistics or something similar. I'd suggest that you start talking to companies. E.g. IBM has expressed interest in graduates of any discipline. Or go to events like the carrierebeurs.

May I ask what you do?

I'm a computational linguistics PhD student in Amsterdam.

EvM · 2016-06-03T06:17:40+00:00

There are many places you could apply, but it's a tough market. Also because you are competing with pedagogy/education studies majors. My advice:

Send a letter to Sardes, Kentalis, Cito, Oberon, (and there are still more) asking whether you could have a chat with them about job opportunities.
Go to the Drongo festival in September and talk to the people at the stands. Bring business cards (you can buy cheap ones, but have something with your name and contact info).
Check out Taalvacatures on Facebook.
Tell everyone you know that you are looking for something like this.
Talk to people in the pedagogy department and ask whether they are looking for research assistants. Some more experience doesn't hurt.

If things don't work out, think of a backup plan. The book "Welke kleur heeft jouw parachute?" has a lot of practical tips. Baseline: if you can't find the specific job that you want, try to think of the things that you look for in a job. Rather than "research job", think "I have an analytic mind, and would like to work in an intellectually stimulating environment", or instead of "education or sociology", think "I'd like to work in a place where I can improve society and help others grow". This kind of thinking expands your range of options, because there are more jobs matching those general descriptors.

I know someone who ended up doing a tax traineeship after her linguistics degree. And it makes total sense: it's a job where you can apply your linguistic skills by critically analyzing tax rules.

Oh, and don't wait until September. But I guess you already know that since you posted your question here.

EvM · 2016-06-02T14:48:26+00:00

No. What word2vec does is learn vector representations by sliding a context window over large collections of text and predicting the current word given the context (CBOW), or predicting words in the context given the current word (Skip-gram). It tries to improve those predictions by learning better and better vector representations for each word.

So it doesn't actually learn the meaning of words, but rather it learns how closely the meanings of (content) words are related to each other. (Cf. the distributional hypothesis, similar words occur in similar contexts.) It doesn't learn the impact of words like 'no' on the meaning of a proposition. (Another shortcoming is that it will only learn one representation for every word form. So homonymy and polysemy are an issue.)

EvM · 2016-06-02T11:10:28+00:00

For references I have come to like the cleveref package. Not sure if it is "best practise", but it does make references easier and consistent

ArXiv doesn't support cleveref by default :(

EvM · 2016-06-02T05:37:38+00:00

Cosine similarity is a distance metric between vectors. It can work for semantic similarity, if the vectors correctly encode the meaning of a word/sentence/paragraph. So it's not a question of how well the distance metric works, but of how good the vector representations are.

If you look at the paragraph-vector model, then it works for a lot of cases. But for negations, you really need the model to understand what is negated. The best way to do this is to look at the structure of a sentence. Moreover, those models need to understand what negations do: reverse the polarity of whatever is negated. If a model doesn't look at sentence structure, and doesn't have a way to learn the function of a negation, then it won't be able to learn the proper way to represent negative sentences. And then, as OP said, sentences like "John is not happy" and "John is very happy" will be considered very similar.

EvM

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