Working with unstructured text for a structured environment, textual, data, recognition
The winning combination of technological advancements, cheap production costs, and Internet has today created a world of massive size and sophistication. Nearly every person, globally, is affected by or directly uses computation on a daily basis. The widespread growth of the use of the computer has profoundly and positively been responsible for affecting the national productivity since the 1960’s. The growth of computing can be measured in two ways: growth in structured systems and growth in unstructured systems.
Types of Unstructured Systems
Unstructured systems do not consist of a predetermined form or structure and usually contain textual data. These systems include emails, reports, contracts, transcripted telephonic conversations, and other written communication mediums. In an unstructured environment, users can structure the message in any desired form, using any language, and the communication can range from a proposal of marriage to a notification of a layoff, and everything in between—without any rules. On the other hand, structured systems are tied closely with the day-to-day operational activities of the corporation. The growth of the structured environment was fueled by the desire of the business world to be competitive and streamlined.
Integrating and Reading Unstructured Textual Data
If a raw unstructured text is placed into the structured world, it will not be meaningful and useful. The key to crossing the bridge between the two worlds in textual analytics is the integration of unstructured text before it is sent to the structured environment. The integration of unstructured text bridges the structured and unstructured data. After this process, textual analytics of the data can be performed.
The process of converting textual data in to electronic media requires a manual scan and correction after the electronic scan is complete. There are multiple options available to do this process depending on the type like audio, text, email, video, and the size of the data.
Selecting a File Type
Due to a variety of file types such as .pdf, .txt, .doc, and .ppt, many third-party vendors supply software and software interfaces that can efficiently and reliably do the data conversion. However, the vendors do not guarantee a 100 percent successful reading.
Harnessing Structured Data from Voice Recordings
Just like scanning textual data, the tapes must be converted into an electronic format. After the source text has been completely read, the text is then integrated to prepare the data for textual analytics. To be effective, textual analytics must operate on an integrated and preconditioned textual data.
Conducting a Simple Search
While conducting a simple search on an unintegrated data, for example while searching for ‘Tommy Lee’, the search does not find references when the name ‘Mommy Lee’ or ‘Tommi Lee’ appears. After textual integration, the search for ‘Tommy Lee’ will provide all occurrences of all the spellings related to the search keyword.
Combinations and Permutations of Words
Integrating the text allows the recognition of the roots of words. For example, while searching for "heating the noodle" on integrated text where the stems of words have been recognized, the results find the following: Heats the noodle, Heated the noodle, and Heat the noodle.
Dealing with the Issues of Textual Integration
During the integration of textual data, some of the key issues must be addressed as follows:
• Determining unstructured document’s relevance to the business
• Removing the stop words
• Converting words to their Greek or Latin stems
• Resolving homographs and synonyms
• The capability to handle both words and phrases
• Allowing for multiple spellings of the same name or word
• Negativity exclusion
• Punctuation and case-sensitivity
• Document consolidation
• Themes of data