Archive for July, 2008:
How To Find A Conaultant
When you are thinking about hiring a business consultant, one of the things that you should remember is that you shouldn’t go with the first consultant that you interview.
The person is going to be an employee of yours, and you conduct several interviews when you are looking to fill a position.
Hiring a business consultant is no different.
When you are looking for a business consultant, instead of just going to the phone book, the first thing that you should do is to talk to others that you have known that have used business consultants.
Find out what their experiences were with their consultants and if they were satisfied with what they did for them.
Before you conduct your interview with your business consultant, figure out what it is that you are looking for.
What is it that you will want your business consultant to accomplish?
Do you want the company to run more efficiently?
Do you want to cut costs?
Do you want your company to make more money?
Knowing what it is that you want to accomplish will help the business consultants that you interview know what type of direction you are going in.
Even if your first interview goes well, keep the appointments that you have made and talk to the other consultants. You may find someone that you like better, or someone that has the same vision for your company that you do.
After you have had all of the appointments, you can make your choice.
Tags: how to find consultant, business consulting
Probabilistic Latent Semantic Indexing
Probabilistic latent semantic indexing is an automated document that is based on a statistical latent model for factor analysis of data.
It is an approach to automatic indexing and information retrieval, which overcomes problems by mapping documents and terms to a LSI space.
Although LSI has been applied with much success in different domains, it has a number of deficits. These are due to its statistical foundation.
One typical scenario of human and machine interaction in the information retrieval is by using natural language queries.
A natural language query provides a number of key words and expects the system to pull up all relevant articles or pages that include the key words.
But the systems are not infallible. Most search engines will come up with a big number of unrelated searches. This is usually due to a key word having two
meanings or where an idea, or multiple uses of key words comes up with many words.
These problems are called polysymy and synonymy. But many of the newer, better-derived latent semantic indexing programs have reduced much of this unneeded search results.
Many retrieval methods are based on simple word matches. It is well known that literal term matching has severe drawbacks.
But newer LSA’s are more specific in their searching and do a much better job than what the old search queries would give for results.
The standard procedure for maximum likelihood estimates a latent variable model as the expectation.
Tags: latent semantic indexing, seo, polysymy
