Ask HN: Any tools to easily cluster ideas/notes by similarity?

https://news.ycombinator.com/item?id=14733562

by arikr • 9 years ago

70 17 9 years ago

news.ycombinator.com

Would like to pass in my Evernote note and output something grouped by similar ideas/notes.

Does this exist?

ehudla• 9 years ago

I wanted to build something like for Zotero. Never got around to doing it, unfortunately.

teapot01• 9 years ago

I am working on something related - unfortunately it's on the far left back burner at the moment. Haven't found anything similar though

Veen• 9 years ago

DevonThink is very good at figuring out related notes. It's Mac only, unfortunately. You'd have to export from Evernote and then into DevonThink, which is an Evernote competitor.

http://www.devontechnologies.com/products/devonthink/overvie...

jamessb• 9 years ago

DevonThink has a "See Also and Classify" feature [1] that will suggest other notes related to a note, or suggest a category for to a note.

[1]: see screenshot here http://blog.devontechnologies.com/2016/11/a-users-journey-in...

mozartoz• 9 years ago

You may be able to craft something like this in org-mode, by writing a little bit of elisp.

org-mode already provides all infrastructure to organise your ideas and tag them. You would just need to do the clustering part.

skate22• 9 years ago

If you know some python and are so inclined, you could use scikit-learn's k-means clustering implementation [1]. I used it to find similar movies based on their plot summaries for a school project.

[1] http://scikit-learn.org/stable/auto_examples/text/document_c...

le-mark• 9 years ago

Long ago, before link counting (googles page rank algorithm) there was quite a lot of research around finding similarity among a group of documents. "Scatter gather"[1] was a method I've often wished I could use on top of a set of search engine results.

[1] https://pdfs.semanticscholar.org/1134/3448f8a817fa391e3a7897...

ollybee• 9 years ago

https://piggydb.net I dont use this myself but it's been on my list of things to investigate for a very long time as it seems like a fresh approch to note taking.

stared2• 9 years ago

If it can be interfered from words alone, try Latent Dirichlet Allocation (e.g. with http://radimrehurek.com/gensim/) to generate tags. Some sources:

* http://blog.echen.me/2011/08/22/introduction-to-latent-diric...

* http://alexperrier.github.io/jekyll/update/2015/09/04/topic-...

* http://engineering.flipboard.com/2017/02/storyclustering

Alternatively, if you know tags, just want to see which are similar to each other, methods like word2vec should help, vide:

* http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html

rspeer• 9 years ago

I think the OP might have been looking for code that already exists, not just techniques for how to build it. But I have some opinions on these techniques.

LDA is not very controllable. It gives you a set number of clusters. Run it again and you get different clusters.

It can't give you documents that are similar to a particular document that you asked for, except to say that all the documents that happen to be in a cluster at the time are similar.

There are indeed up-to-date things you can do with word vectors -- though I'm sad that the tutorials always point to word2vec or GloVe, as if it's 2014. That's ancient in machine learning terms, and we now know of flaws in their outputs, such as [1].

If you want downloadable, pre-computed word vectors, ConceptNet Numberbatch [2] (part of the ConceptNet project that I develop) is the best in class right now, and you can download it in the same format as these older systems. And if you don't believe me tooting my own horn, at least use something else that's been updated in the last year, such as NASARI, or maybe fastText's precomputed English vectors.

Or you can just compare "bags of words", which is still good enough for many applications and readily available out of the box. I believe this is what you get with a "More Like This" plugin for a search engine such as Lucene, for example.

[1] https://arxiv.org/abs/1607.06520

[2] https://github.com/commonsense/conceptnet-numberbatch

stared2• 9 years ago

It's true that the number of topics is a free parameter of LDA. Yet, maybe unavoidable if we want to tweak between just looking at similar words vs at very coarse patterns.

For document similariaty tools typically does not be that subtle as for analogy tasks. Thank you for pointing to your research/tools anyway. :) And well, word2vec/GloVe are by no means the end of story - just a simple baseline.

evolve2k• 9 years ago

Different tool but for anyone doing a bunch of academic research and wanting to group what they are learning using an open source tool; check out docear.org

They could really do with more developer support also. Had difficulties finding someone to build a LibreOffice plugin and a (Mac)Word Plugin.

cartercole• 9 years ago

https://radimrehurek.com/gensim/ makes topic modeling really easy in python

sciencerobot• 9 years ago

There is an option/plugin for Jekyll for finding similar blog posts. https://jekyllrb.com/docs/configuration/

projectramo• 9 years ago

I assume you mean something that does it automatically, but you should also know that you can tag notes, which would be a manual way of achieve this.

(For developers: just take the most frequent words that are not "a, the, if, but" etc and convert them to tags)

bryanph_• 9 years ago

I'm currently working on a graph-based approach to this problem. I wrote about it here: https://hackernoon.com/building-a-open-source-personal-knowl...

SeaDude• 9 years ago

Emacs org-mode. Plain text for everything, for the rest of your life.

Ask HN: Any tools to easily cluster ideas/notes by similarity?

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