Contextual searching and natural language processing
How have you ever tried to find any information on the Web or in a large document without any positive results? I am pretty sure that you did. Something that is clear for us may not be clear for the search engine that can be web-based or document-based. The reason why it is sometimes difficult to find relevant information is that not all tools are using contextual searching but rather simple searching engines that are using keywords without matching them with the actual intention of the user.
Let’s jump to an example to better illustrate the difference between these two methods of searching for content. If you are looking for specific information about Company’s X financial results you may put a phrase ‘Company’s X financial results. A simple keywords-based engine will divide this phrase into keywords and will present you with much irrelevant information about financial statements, company X (not financial data), maybe some about results (whatever it is), and financial topics. More advanced engines are using context and in the above case they will filter all searches to find the exact information you are looking for – financial data for Company X. As a result, you will receive only data that is relevant for a particular case.
Contextual or semantic searching has at least a few methods that are used to ‘retrieve’ the actual intention of the user or substance of the inquiry, however, it is not relevant for business applications as we are applying a relevant method that is appropriate for the particular use case. The idea is to truly interpret the will of the user and to provide him or her with ‘ideal’ results.
This approach has many benefits for the user. It significantly shortens the time that is required to get specific information and not to provide irrelevant information. If your search engine that can be embedded to your natural language processing tool (or similar) may search the whole database (that may include thousands of documents) and find specific information, link with all documents (or even find interlinkages), and prepare comprehensive reports.
Contextual searching is, however, not always the best option and sometimes basic search engines are sufficient. Semantic searching is the most appropriate option if you are dealing with unstructured data (e.g. plain text, e-mails, pdf files) where the context may be (and usually is) relevant and one keyword or phrase may have a different or similar meaning in various documents. It will not, however, be effective to use such an advanced tool if you are looking for quite simple information where the context is not that important, e.g. basic research on a particular topic.