Natural Language Enterprise Search Improves Info Gathering
Most organizations have some form of search platform at the office, and it’s something that you probably take for granted. You’re used to typing in a file name into a search bar and find it. And if you can’t, you just ask the person in the office next to you for help.
What if I told you there was a way to improve upon your enterprise search method? A tool that would surface the most relevant results, answer your questions and keep learning what you need every time you click on data it shares with you? Long used in web search engines, the power of natural language processing is now changing the way organizations find and reuse institutional knowledge. Natural language enterprise search, such as the new Amazon Kendra search platform, has upped the ante for enterprise search. Let’s take a look at how it works.
Enterprise Search Gets Smarter
It all comes down to machine learning. It’s helped enterprise search evolve and become smarter and more relevant. You typically would type in simple keywords to find what you’re looking for. Now, with some of the new search platforms, you can ask questions and the tool’s natural language processing capabilities will surface the most relevant results for your needs.
These natural language enterprise search platforms use deep learning models to understand your users’ intent to find precisely what they are looking for.
This relevance is what drives modern, natural language enterprise search toward the future. New solutions like Amazon Kendra are able to connect to multiple repositories to give you widespread results. These include Dropbox, Sharepoint, Salesforce, Amazon S3 and other popular source systems, websites and relational databases. The best of these support unstructured and semi-structured data in .html, MS Office (.doc, .ppt), PDF, and text formats.
These natural language enterprise search platforms use deep learning models to understand your users’ intent to find precisely what they are looking for. These models also understand the topics of documents, so users don't have to search using the exact keywords in a document. And if these models are able to find an answer in a document that matches the intent of the user's query, they provide that answer directly so users don't have to spend time reading through a long list of results.
These models are also trained to understand the language and terminology of key industries (such as healthcare and financial services) allowing users to search more naturally in their context.
Next-generation search platforms also have a social dimension. They are able to learn from user behavior and take feedback into account. Every time users search and select a result, the search engine learns which results are most relevant. And they can also take feedback into account when users click "thumbs up" or "thumbs down", indicating whether the machine learning models got it right.
What Does this Next-Generation Enterprise Search Offer?
Machine-learning-driven search platforms like Amazon Kendra give users a range of features that work together to surface the information they need when they need it.
Natural language and keyword support
The ability to understand natural language queries is at the heart of machine-learning-driven search solutions. Natural language understanding gives users the ability to ask a question and get an answer to their question. Sometimes, users really do intend to do a keyword search. In that case, the search engine defaults to providing a more typical list of highly relevant results.
Reading comprehension and FAQ matching
When users have a question, they usually don't want to read through all of the documents returned in the results. They just want an answer, as though they were asking a colleague. That's the power of Natural Language Understanding.
If the search engine finds a passage in a document, which it "believes" matches the user's question with a high degree of accuracy, it simply presents the answer, along with a list of documents that may also answer the question. You also can upload organizational FAQs so common questions are answered quickly and accurately.
Document ranking
A deep-learning, semantic search model allows modern platforms to return a ranked list of relevant documents. This allows end-users to have an exhaustive list of information if they need to dive deeper into search results.
Domain optimization
The most advanced enterprise search platforms can understand language and queries for both multiple internal use cases and also specific industries where language might be complex. Use cases could include HR, support and operations, while industries could include legal, healthcare, oil & gas, automotive, for example.
These are just a few examples of the power of natural language enterprise search that’s driven by machine learning. Finding the institutional knowledge you need quickly and securely is a must in today’s digital workplace. Your search solution should help you do that, not slow you down.
Are you ready to see how this new generation of enterprise search solutions can help you and your remote workforce?
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