Natural Language Processing & the Semantic Web

Source: http://www.MyRoar.com

What is Natural Language Processing and Question Answering Search?

Natural Language Processing (NLP) is the technology that evaluates the relationships of
words such as actions, entities, or events, comprised within unstructured text, meaning
sentences within paragraphs found in a variety of text based documents. Question
Answering Natural Language Processing Search is the Natural Language Processing
technology that specifically solves the problem of finding answers to a question which
can be asked by simply entering it into a search interface using natural human language,
for example, “Who is Barack Obama?”

Unlike keyword search in Google or Yahoo for example, Natural Language Processing
Question Answering Search specifically allows users to ask questions in their natural
language and then retrieves the most relevant answers within seconds. The standard
search process requires the execution of multiple keyword combinations that then force
the searcher to click on links only too frequently to find no answer and then they process
of searching and liking continues until the user finds something or gives up. With
Natural Language Processing Search there is no extra work and no need to search
multiple links, resulting in immense time savings. Entering a question is simple for the
user even though the technology behind the scenes is highly complex.

Why Natural Language Processing is a Critically Needed Technology

Anyone who has used a search engine to perform market, consulting, or financial
research, can tell you the pain of spending hours looking for the answer to a seemingly
simple question. Add up all the questions a researcher must ask and the hours really rack
up.

Just how big is the search problem? According to International Data Group the average
knowledge worker makes $60,000 per year out of which $14,000 is spent on search.
Knowledge workers spend 24% of their time on search. Here is a quote from Network
World, "A company that employs 1,000 information workers can expect more than $5
million in annual salary costs to go down the drain because of the time wasted looking for
information and not finding it, IDC research found last year." Furthermore an Accenture
study found that 50% of information retrieved in search by middle managers is useless.

In the document heavy financial services sector researchers are frequently forced to give
up looking for answers, or cannot check the accuracy of answers with multiple sources
because it would be time prohibitive. Senior risk management is comprised of a firm’s
most senior executives whose job is to evaluate if you are doing your job correctly to
mitigate risk at the most upper levels of the firm. Now imagine you are on the phone
with your firm’s senior risk managers (your boss’s boss’s boss) and you are asked a
question that you don’t know the answer to? Imagine if you could type a short question
into a search box and come up with an answer in time to provide an intelligent and
correct response to the question? That is the power of natural language processing, you
type in a question in “natural language” and be provided with an instant result containing
the answer that saves the day.

How Does Natural Language Processing Work

Natural Language Processing builds off of a statistical relationship tree that shows the
difference between “My friend Blair the singer” and “Author Blair Singer.” In Natural
Language Processing Search case one, “My friend Blair the singer” here “Blair” and
“singer” are one node or branch away in the statistical representation. In case two “Blair
Singer” here “Blair” and “Singer” are directly linked in the statistical representation.
With the new technology built around statistical representations, Natural Language
Processing Search technology can now reach precision of up to 95% given a full index.
Precision is the measurement of how many top search results are relevant to the question.
Have you ever searched by entering two key words into a search box? Of course you
have. You are trying to find information on two related variables. Someone might ask,
“How many hedge funds are there?” Here the user is asking how many investment
firms “exist” that are classified as a “hedge fund.” Try any search engine and not one of
the dozens of search engines, except MyRoar, can provide an answer without requiring
further research.

With this question “How many hedge funds are there?” today’s search engines provide
no answer whatsoever, even if you read through the pages of resulting links. Here in lies
the problem, if you ask “How many hedge funds are there” you are likely to find an
article about Hedge Fund performance, Hedge Funds in the news, but not one answer will
give you the actual number of Hedge Funds. Regular search engines provides no
technology that understands the difference between “Hedge” the investment fund and
“Hedge” the bushes and cannot relate the context to understand you mean “Number” as
in number of funds versus “Number” the number of phone calls from telemarketers.

How Natural Language Processing is Different than Semantic Search

Frequently associated with Natural Language Processing Search is Semantic Search, a
related but actually very different technology. Semantic Search is designed to uncover
meaning and can provide researchers with information extracted from unstructured text
about sentiment, recording instances of “positive” and “negative” words and the
frequency with which such sentiment surrounds a specific entity or event. For example,
in semantic search you can evaluate how many times Microsoft had positive mentions in
a span of articles. MyRoar has developed a Natural Language Processing Question
Answering system that differs immensely from semantic search and other similar
applications, because rather than looking for sentiment, we look for specific answers to
questions that range from simple to very technical in nature.

Why is Now the Time for Natural Language Processing

For almost 20 years the evolution of this technology has been expected to progress to
point where Natural Language Processing Search can filter out a majority of irrelevant
results. In the last two years we have seen major advancements that means some of the
behind the scenes Natural Language Processing technology is scalable and commercially
viable for the first time.
MyRoar is the Ultimate Filtering Solution Using Natural Language Processing
Natural Language Processing is a hot technology in today’s world where we have an
overload of information and the inability to adequately filter it. Because Natural
Language Processing provides this filter it is the real answer to the problem, not semantic
search, and not because semantic search is bad, but because semantic search solves a
different type of problem which is sentiment. The two are often confused. With natural
language processing you can answer any question as fast as you can type it. You will no
longer have to scour several pages of search results, clicking through links, just to find it
is not the right link. This is why MyRoar, Inc. provides something that people and
businesses need, want, and must have with 95% precision and accuracy which is
“astonishing” according to an industry analyst. We call it MyRoar because we provide
the power to make you ROAR.

Kate McDonough
CEO & Founder
MyRoar, Inc.