Leveraging Machine Learning With Enterprise Search
Leveraging Machine Learning for Enterprise Search
The Google search index contains one of the world’s largest collections of structured data. Every day, people use Google to find relevant information among billions of documents — and every day, Google’s results get a little better. Google utilizes machine learning to improve its search results. If a person clicks a search result and immediately uses the “Back” button, it’s likely that the search result doesn’t contain information relevant to the query. Over time, Google’s results for that query improve.
Machine learning has a proven track record in enabling companies to make sense of massive amounts of data. If your organization needs the ability to rapidly classify, locate and draw conclusions from a large store of data, machine learning may provide a viable solution.
Let’s examine some of the considerations that may impact your company’s decision to add machine learning features to a document search.
Machine Learning Handles Complex Classification
Machine learning works well for document collections so large that they defy rule-based classification. If a classification system needs to consider many different aspects of a document — and how those aspects overlap — before classifying that document, manually creating a set of rules to account for all of the variables becomes difficult. By giving a machine learning system an initial set of rules and refining those rules with training instructions, you can handle complex document classification with less programming effort.
Machine Learning Can Scale
Implementing a machine learning system for enterprise search requires money and time — and the effort is really only worthwhile for very large projects. If your organization only needs to classify a small number of documents — and humans could do the work in less time than it would take to build a machine learning solution — then classifying the documents manually is probably the better way to proceed. Machine learning is ideal for tackling large, complex projects in which human effort alone wouldn’t be enough to complete the task quickly.
Training a Machine Learning System Successfully
To implement a machine learning system for enterprise search, you’ll begin by training the machine learning system with a data set. Expert input is vital during the training phase because you’ll need to give the machine learning system training data that’s large and varied enough to establish a viable classification model that the system can use when processing future data. Data quality also plays a role in ensuring that the machine learning system continues to improve. The regular human review is necessary to help the system learn from its mistakes.
If you want your machine learning system to draw conclusions from the data it analyzes, the data must contain correlations — and finding correlations in unstructured data can be difficult. Language processing — the ability to understand the meanings of words and the semantic relationships between them — can help a machine learning system draw conclusions from documents that have little in common with one another.
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