Thing Two drew this for me this morning. I love her vivid imagination!
In a recent comparison of different text extraction algorithms, Gravity’s open source project: Goose tied for second place and was even written up over at Read Write Web! I find this very exciting because our project is still quite young and actively in development whereas the algorithms in close standing are mostly well established and semi-finalized. Another interesting point is that most of the competition was built by teams of researchers, you know… Doctors in their fields!
The graph below from Tomaž Kovačič‘s study shows only a small amount of the data he collected in his analysis. If you are curious of how he compared these algorithms, I highly recommend you head over to his post. He does a great job exposing the details behind his analysis.
Goose’s wiki provides a very detailed explanation about what Goose is and how it works, and also touches on the original need we had at Gravity behind its creation. Jim Plush wrote the first version from the ground up on his own and only recently gave me commit access to the repository. By the time I got into the project, it had all the bells and whistles required to compete in the analysis completed by Kovačič. My contributions to Goose have been to extend it to allow for more specific extractions of additional meta data outside of the primary content and have no effect on its standings above.
Such a utility can be applied to a wide variety of web content analysis problems, and I’m really glad Plush decided to share it with the rest of the open source community. At Gravity, we have been building a lot of exciting (to me at least) technology and most of it is held dearly by us and needs to remain a company secret as they make up a large part of our company’s overall value. When it comes to analyzing the content out here on the web, Goose can be looked at as our trusty messenger delivering our system plenty of content to analyze without a lot of the noise that comes along with it on the web pages the content is sourced from.
If you are looking to mine some of the golden nuggets of information that is buried under a ton of ads, peripheral links, site menu structures, and other distracting noise, then why not take a look at what Goose has to offer? If you find anything you think Goose may be lacking or have some ideas on anything else that may be improved, let us know on our Github repository: https://github.com/jiminoc/goose
So last night while I was traveling home via public transit, I was also trying to keep in contact with my wife via instant messaging. This is a common practice for my wife and I so that I can enjoy my trip more and she can know that I am safe and getting closer to home.
Well things seemed fairly normal in the conversations I was having except it did seem that she was having a harder time than usual understanding what I was saying. I had a long wait between buses and I was getting pretty hungry, so I started instant messaging questions about my dinner options for home. In come responses from my wife that they had “GoodStuff” for dinner and when I asked if there was any left for me, a resounding “Yes!” with smileys came back. I knew that they must have finished eating hours ago, so I made a request for her to start reheating it so that I could eat quickly and then move on to doing bedtime for our two daughters. I was so happy when a quick IM response came back saying: “Sure! OK!” and again a long line of various smileys.
More small chat continued until I arrived home to find an empty table and empty stove. Although it was nice to see that everything was so neat and clean, I was a little disappointed that there was no hot dinner for me after the IM conversation we just had. I then noticed that my wife was busy with our laundry and my 7 year old daughter (aka: Thing Two) was next to her holding my wife’s phone (we use our phones for instant messaging). Not only was Thing Two happy to see me, she was also laughing a lot more than usual. I asked my wife about the IM conversation we had moments ago, and she looked a little confused. This is when Thing Two jumps up and says: “I fooled you Daddy! You thought I was Mommy!” We all had quite a laugh.
It was just shocking at how I was not able to notice the difference. My wife tends to be very terse in her IM communication, so it did not seem odd for me to ask a long question and then receive a small “ok” response. Boy has Thing Two come a long way in her pranks. I’m both proud of her and a little scared for what we’re in for as well.