5 Ways Text Analytics and NLP Make Internal Search Better
Implementing AI-driven internal search can significantly impact employee productivity by improving the overall enterprise search experience. It can make internal search as easy and user-friendly as internet search, ensuring personalized and relevant results.
Following are some solutions that can accomplish this:
Semantic search has meaningfully impacted the usability of the web by understanding the intent and contextual meaning of the words a person is using, resulting in the delivery of much more relevant results. Until now, semantic search has not been available to internal search users for multiple reasons:
♦ Numerous disparate systems in an organization make implementing semantic search across them a daunting task.
♦ The majority of critical information is buried within documents.
♦ Applications are not integrated and there is no single search interface.
Semantic search requires the following to work with internal search:
♦ A user interface that takes advantage of natural language query to enable users to “search as they speak.”
♦ Indexing/ingesting connectors from third parties such as BA Insight for critical systems to create a single unified index.
♦ NLP platforms such as Microsoft Cognitive Services, Google Cloud, Amazon Comprehend or Open Source to provide a deep understanding of the concepts within documents to add intelligence to the index.
The following are some opportunities that the above strategy enables:
♦ Use of natural language to search across multiple systems rather than using different user interfaces and search functionalities for each system.
♦ Finding the right documents based on concepts within them as opposed to their filenames/titles.
♦ Finding images or documents that contain images using the additional metadata.
♦ Discovery of videos through automatic creation of transcripts.
When done correctly, semantic search delivers highly relevant search results by providing them based on user intent as opposed to keyword search, where there is no understanding of user intent.
Machine learning works well on the web as internet traffic generates large amounts of data that can be used to understand user behavior patterns and make relevant recommendations. This is not the case with internal search because the amount of consolidated data is limited and often stored in multiple applications.
Connecting the most important internal systems to a single index with a single UI solves this challenge. It makes it possible to capture search data and take advantage of machine learning to improve the user’s search experience and make it more internet-like. Examples include:
♦ “Search as you type,” which presents information based on previous searches and content suggestions based on location, project, department, etc.
♦ Automatic correction of search queries based on previous productive searches.
♦ “Viewed by others” personalizes users’ search results based on information about them such as their location, department or interests.
A single index that unifies vast amounts of organizational data enables machine learning to take advantage of user search patterns and proactively provide recommendations and results that improve relevancy, similar to that on the web.