Reposting here because someone correctly pointed out it is better suited for here.
So I have a bunch of titles of eBay listings. I want to extract product information from each title, so I can categorize the listings by product since these titles are coming from wide-ranging searches.
The are the following Titles:
- 5 PCS 3 Digits 0.56" NUMERIC DISPLAY 7 seg segment Common cathode
- 5 PCS LD-3361AS 3 Digits 0.36" NUMERIC DISPLAY 7 SEG COMMON CATHODE digit
- 6 x MAN71A Common Anode Red Led 7-Segment Display (6 pcs)
- Experiment Board Breadboard Circuit Board ZYJ - 60 White NEW
The format in which I want to extract the data is as follows:
For the 1st listing:
There are 5 pieces It has 3 digits It is of size .56" It is 7 segments It is numeric It is cathode It is a display
For the 2nd listing:
There are 5 pieces It is of size .36" It is model LD-3361AS It has 3 Digits It is Numeric It has 7 segments It is cathode It is a display
For the 3rd listing:
There are 6 pieces It is brand MAN71A it has 7 segments It is Anode It is a display
For the 4th listing:
It is New It is a Breadboard It is White It is model ZYJ - 60
The reason I want to learn machine learning to do this is that it would make it very easy to just take those specifics and store it for later use and because it can adapt to any search and not just in a specific category. It would also be able to adapt to different items or formats in the title. I tried this with scikitlearn's kmeans, but it gave me a lot of overlap and just clustered them, rather than extract the details from it. What I want to do is have the program look at the title, identify specifics as what they refer to (i.e., size, color, brand, etc.) and extract the data. Ideally, this would be unsupervised learning, but as I write this, I start to realize that may be impossible to do with this. The part I'm stuck on is what to use to achieve this. Should I use NLTK? One of the classifiers from scikitlearn? Clustering?