The six months rule says that every programmer should look at what he was doing 6 months ago and be disgusted about the way he was doing things.
If you’re a programmer and you look at your code from 6 months ago and you’re still doing the exact same thing today: Please Stop whatever you’re doing and go learn something new.
It boggles my mind that MS Office can be so different between Windows and OS X!
Is this Microsoft or Apple? I’m guessing it’s Microsoft because they are the fools that can’t make it a similar experience on both.
As someone learning Scala, I wanted to learn the basics of doing builds with SBT, and when I was asked to do a small Java project it seemed a good opportunity to do so. However, as I quickly found, SBT is naturally focused on Scala and so needs a few extra build settings to get a java project…
Back when Firefox was my primary browser (before switching to Safari and Chrome), I loved the Tag Bundles View of the the Firefox Delicious extension.
Well, I’ve finally published a Chrome Extension that I have been playing with, which reproduces some of that functionality. The Delicious Bundles Bar extension for Chrome will synchronize your del.icio.us bundles as folders in the Chrome Bookmarks Bar.
The Delicious Bundles Bar is based on the version of the Delibookmarks Chrome Extension (a.k.a. Chromicious), which synchronizes your Delicious bookmarks with Google Chrome and keeps them in sync for easy access.
You can get the Delicious Bundles Bar extension for free in the Chrome Web Store.
Update: Apparently the current version only works on Mac. Sorry Windows users. I’ll have to dig a little deeper to see what’s wrong. No idea how it behaves on Linux.
First thing’s first–what is coreference resolution?
Co-reference means that multiple expressions in a sentence or document refer to the same thing. OpenNLP contains a “linker” that analyzes the tokens of a sentences to identify which chunks of text refer to the same things (e.g., people, organizations, events).
Take, for example, the sentence “John drove to Judy’s house. He made her dinner.” In this example both John and He refer to the same entity (John); and Judy and her refer to the same, different entity (Judy). Don’t expect OpenNLP to get this 100% correct. Even a simple example like this is a difficult problem.
Picking up where I left off once upon a time (and finally wrapping up this series), here are links to the old material:
- Getting started with OpenNLP – Sentence Detection and Tokenizing
- Part-of-Speech (POS) Tagging with OpenNLP 1.5.0