GATE, NLTK: Basic components of Machine Learning (ML) System by Krishna Prasad.
From the post:
I am currently building a Machine Learning system. In this blog I want to captures the elements of a machine learning system.
My definition of a Machine Learning System is to take voice or text inputs from a user and provide relevant information. And over a period of time, learn the user behavior and provides him better information. Let us hold on to this comment and dissect apart each element.
In the below example, we will consider only text input. Let us also assume that the text input will be a freeflowing English text.
- As a 1st step, when someone enters a freeflowing text, we need to understand what is the noun, what is the verb, what is the subject and what is the predicate. For doing this we need a Parts of Speech analyzer (POS), for example “I want a Phone”. One of the components of Natural Language Processing (NLP) is POS.
- For associating relationship between a noun and a number, like “Phone greater than 20 dollers”, we need to run the sentence thru a rule engine. The terminology used for this is Semantic Rule Engine
- The 3rd aspect is the Ontology, where in each noun needs to translate to a specific product or a place. For example, if someone says “I want a Bike” it should translate as “I want a Bicycle” and it should interpret that the company that manufacture a bicycle is BSA, or a Trac. We typically need to build a Product Ontology
- Finally if you have buying pattern of a user and his friends in the system, we need a Recommendation Engine to give the user a proper recommendation
What would you add (or take away) to make the outlined system suitable as a topic map authoring assistant?
Feel free to add more specific requirements/capabilities.
I first saw this at DZone.