Reputation: 93
As for college project I am required to build a software that, given some comments concerning a virtual construction site, detects its actual state (just started
, in construction
, terminated
).
For example, given the comments:
The system should detect that the "English Channel bridge" construction site has ended.
At the moment I'm trying to choose what word classification algorithm to use for this project. I searched online looking for the best classification algorithm to use. I've read about SVC
but, since I'm not really an expert in this field, I am unsure about the compliance/goodness of SVC with my scenario.
What I'm trying to obtain is not the solution to my problem, but a list of available algorithms with their pros and cons.
Upvotes: 3
Views: 157
Reputation: 1070
You are formulating your problem incorrectly, making it difficult for people to give you a list of pros and cons.
The problem you are describing is not really a word classification problem since you are not classifying words. What you are trying to do is:
The algorithm is not the real issue. Most classification algorithms (linear regression, decision trees, SVM, etc...) will work.
The problem you actually have (but don't realize based on your question) is that you have no training data for either finding construction project named entities or classifying those entities once you have them into your 3 categories.
My suggestion would be that you use one of the freely available NER toolkits/libraries out there, add in dictionary features related to construction projects (words like bridge, tower, etc...) and see how well you can do at the first part of your task.
More important considerations are:
I'm sorry, I realize this is probably not the answer you want to hear but I suspect it is the answer you need. ;)
Upvotes: 1