Unstructured data is the term used to describe the collection of data that is not organized in any pre-defined format or in a database. Unstructured data is usually created with the intention of direct human consumption; it can include email conversation, conversation transcriptions, audio files, photos, web pages, pdf, and old-fashioned paper documents. It is estimated that over 80% of the data available in an enterprise is considered unstructured.
With all of this unstructured data flowing in and around an enterprise, it is no wonder why it is so difficult to create the perfect Artificial or Business Intelligence application. In machine learning systems, runtime efficiency depends mostly on the ease of access to data to ensure faster processing. Therefore, there is a need to convert the already collected unstructured data into a structured form to improve both the stability and the functionality of the system.
This article will try to provide an overview of some of the most dynamic methods of collecting, extracting and converting non-structured data in a machine learning environment.
Human in the loop Learning extractors – These are the type of extractors that are used to collect data from human behaviors. They process cues that come from their human partners and collect that data in a structured form. For instance, AI personal assistants that learn from their owner’s character wants or tendencies and then can make predictions on what the owner might need in a future circumstance.
Value Matching extractors– these are the kind of extractors that can find a set of values in a pre-defined search area. Web scraping tools are a good example of this technique. You can also use this when you have massive amounts of unstructured documents, and you are trying to extract specific information that you know exists or if you feel that converting the data is inefficient and non-economical. Value matching extractors amalgamate data and store the most valuable sets of the data.
Visual Pattern Extractors – these extractors extract data from a visual representation of non-structured data. These may be in the form of images, charts, graphs or computer graphics. For instance, they may evaluate an image and check the time it was created, weather condition, the nature of the event and henceforth.
Table Extractors – In any enterprise, a lot of transactions are tabulated in tables. Table extractors are specifically used to collect and structure this data in a form that may be used by machine learning algorithms. It is then clustered together in databases accessible to AI algorithms to use.
Semantic Extractors – Unstructured can data contain industry-specific jargons that may prove confusing when fed into the Machine learning system’s data banks. The semantic extractors help in changing the semantic into something machines can understand. Also, when these non-structured data elements originate from voice conversations, semantic extractors must be equipped with robust natural language processing algorithms to collect and sort the data provided.
Package Form extractors – Questionnaires, surveys, and business forms are some of the most common product and services assessment tools in an enterprise. Though the data collected can provide mass amounts of insights to the users it is not always collected in a usable and structured way so that we can add it to our machine learning toolsets. Form extractors are used to provide the clustering of this data, making the conclusions and storing the data in the backend for use by machine learning systems.
Machine learning systems are becoming ubiquitous in every modern day enterprise. In many cases, to make our AI systems fully functional, we need to give them access to the millions of Gigabytes of data generated every day that is non-structured. Using some of the techniques mentioned above will help us do just that.
This article has been updated on the Empaur website The 6 Fundamental Machine learning techniques for Recovery Audit