High-reproducibility and high-accuracy method for automated topic classification

http://amaral-lab.org/publications/high-reproducibility-and-high-accuracy-method-automated-topic-classification/#.VMzwjSiN1WU

by jestinjoy1 • 11 years ago

142 30 11 years ago

amaral-lab.org

matt40773• 11 years ago

I had some sort of violent dopamine release just reading the headline.

I'm working on a project to make (EU-) law more accessible. So if anybody here knows good methods to visualise/summarise long legal texts (30-300 pages) you could do something for humanity by posting a reply.

(Word clouds just don't cut it in these cases.)

miket• 11 years ago

Check out http://casetext.com. Their "ReCites" extraction is a pretty useful way of summarizing a legal case text.

matt40773• 11 years ago

Excellent! It seem to heavily rely on the community & moderation – something we're exploring with as well. Kinda like (rap)genius.

mjohn2• 11 years ago

Is there a link to your project? I've recently been thinking of trying something similar for UK law.

matt40773• 11 years ago

It's not online yet. But check out popvox.com, govtrack.us and http://openbylaws.org.za/ for some inspiration.

mikecb• 11 years ago

Haven't looked at EU law in a year or two, but europa and eulex used to provide useful and simple summaries of both legislation and court of justice caselaw.

alexwestin• 11 years ago

Contact Kasian Franks kasian.franks@gmail.com he has a good summarization system I'm using.

bane4• 11 years ago

A classic summarization method is:

1. Split your text into sentences

2. Remove stopwords (keeping a copy of the original sentence)

3. For the remaining words, calculate the synthetic TF-IDF score of all the words in the sentence (tf-idf each word then sum them).

4. Keep the n-highest scoring sentences (in the order they appear), or all the sentences with synthetic TF-IDF scores above some threshold.

There's your summary.

riazrizvi• 11 years ago

I find it works better with additional step 2.5) Lemmatize remaining words, using for example Python's NLTK library.

peter_l_downs2• 11 years ago

I built this once! If anyone wants to play around with it the website is http://bookshrink.com and the code is on github (https://github.com/peterldowns/bookshrink)

cafebeen• 11 years ago

This might be helpful:

http://www.cs.ubc.ca/labs/imager/tr/2014/Overview/

It includes a number of the data mining techniques others have mentioned and implements some nice design guidelines from visual analytics.

ntoshev• 11 years ago

What is the user supposed to learn from the visualization? Is it high level understanding what the text is about? Is it some support to make the text easier to read and understand (like syntax highlighting is for programmers)? Is it something in between, something else?

b6• 11 years ago

I haven't dug into the details of the paper yet, but I want to commend the authors for 1.) making it possible to actually download the PDF and 2.) giving some indication, within the actual document, when the paper was published. I'm being a little bit snarky, but I'm very sincere in thanking them.

kleiba• 11 years ago

It's usually not that the authors don't want to provide PDFs but with scientific journals you're often asked to sign a form that transfers the copyrights to the journal. In other words, you're not allowed to make the paper available even though you're the author.

In my experience, finding out when a paper was published is often not too difficult. In most cases, Google makes it easy to find the Journal issue and/or the conference proceedings of the paper. Or you find some third paper that contains a reference with date information that you can then use to double check.

rcpt• 11 years ago

making it possible to actually download the PDF

The journal they published in, Physical Review X, is a newer open-access journal from APS (along the same lines as PLOS ONE or Nature Scientific Reports). I think it's great but not everyone agrees. To read more on the debate around the open-access phenomenon look at http://blogs.berkeley.edu/2013/10/04/open-access-is-not-the-... and http://www.sciencemag.org/content/342/6154/60.full

vilda• 11 years ago

The PDF link works perfectly for me: http://amaral-lab.org/media/publication_pdfs/PhysRevX.5.0110...

b0b0b0b• 11 years ago

I'm confused by the discussion of multi-lingual corpora. Is it common in topic modeling to consider documents drawn from disjoint vocabularies, or is it just a kind of thought experiment?

3pt14159• 11 years ago

Pretty common when you don't control the data source or for multi language goverment agencies (for example in Canada you may have your court case in French if you desire).

curiously• 11 years ago

is there an open source implementation I can use?

What about that sentiment analysis NLP tool that someone posted on HN last year? That was also very good.

abeppu• 11 years ago

I find it interesting that this appears to be written by a group of physicists rather than NLP or ML researchers, and I think you can kind of see that in the way they approach the problem. I think a bunch of the work done after LDA among ML and NLP people tended towards (a) using Hierarchical Dirichlet Process models as a platform from which to explore Bayesian nonparametrics more generally (b) better inference algorithms for topic models and (c) somewhat richer models (i.e. author topic models, syntax aware topic models, etc).

And it's not like the people in this field haven't been aware of network-oriented methods. But rather than using community-detection as a mechanism for topic discovery, instead people either focused on networks among topics to see how topics are related, networks among authors such that social network information informed topic discovery, or networks among documents where link/reference information was explicitly part of the model.

These authors seem to get solid results in part by having totally different values/aesthetics. Unlike the Bayesian nonparametrics people, they clearly don't care about picking arbitrary, inflexible parameters (e.g. the 5% threshold), nor do they want their model to have a clear, generative form, nor are they particularly concerned about having a new algorithmic insight (since they throw their hard work to InfoMap, and discuss none of its details), nor do they attempt to advance the expressiveness of their topic model (they proceed with the most basic bag-of-words model available). But it does seem like they get good results on the basic task with a very pragmatic, pipeline approach.

shanusmagnus• 11 years ago

Two of the authors (Kording and Acuna) are definitely not physicists; much of their previous work you might describe as psychology with a strong math modeling background. Interesting that the pub is in a physics journal though.

avyfain• 11 years ago

You can see the source code of a previous iteration of the algorithm here: https://bitbucket.org/andrealanci/topicmapping/src

helderts• 11 years ago

Modeling words co-occurrence graph and then pruning "weak" edges (or achieving similar pruning by using community detection to find clusters) works kind of like a "feature selection" based on something that resembles a bare mutual information or tf*idf.

I'm not entirely familiar with LDA, but from what I was able to understand from their intro, it feels like their LDA application could have used some feature selection.

jetsnguns• 11 years ago

It was interesting to see a take on the problem from the researchers outside of NLP or ML fields, but the authors only considered classic LDA and PLSA for comparison. I am not currently involved in topic modeling, but I know there exist techniques and modifications to classic models that improve topic discovery (like tf-idf weighting). Can you suggest any modern methods from NLP and ML communities that address the same issues and can rival the authors' findings?

High-reproducibility and high-accuracy method for automated topic classification

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