Linguistic Tools Usage in Managing Corporate Data

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yana-yelinaIn this special guest feature, Yana Yelina from EffectiveSoft, discusses the problems and potential solutions in enterprises to address a rising volume of documents and the ability to effectively utilize data stored in document silos. Yana Yelina is a website design and development expert at EffectiveSoft, a custom software development company with 250+ specialists who boast expertise in different business domains.

Business intelligence trends in the modern world set us free from document overloads. However, a considerable number of business people continue storing their corporate knowledge in separate textual documents, thus, creating a so-called corporate memory. The survey found HERE defines this term as “the collective data and knowledge resources of a company” that may include databases, electronic documents, reports, product requirements, design rationale, etc.

With the existence of handy high-volume disk and cloud storage, users forget about the huge amounts of information within the organization, which leads to a dramatic growth of documents on employees’ computers and in the corporate network. Thus, useful and comprehensive knowledge seems to be dispersed among a broad array of separate documents with no direct relations.

To address the challenge, we are to turn to specialists in custom business intelligence solutions who elaborate specific document and knowledge management systems. But the practice shows that such systems have similar disadvantages. Let’s go deep into the problems users face in this case:

1) Document versions. If the organization doesn’t use a special system to control versions, the corporate workflow system may have a number of similar documents with slight differences. A fear to lose important documents makes employees store all the versions and get mixed up with their sequence.

2) Workflow failures. Documents in their essence are not sufficient information sources. They are really vital within the framework of business communication, additional materials exchange, comments and adjustments. If one of the workflow elements is broken, it becomes difficult to restore the set of materials and relations between them.

3) Poor document search. Some document management systems don’t allow to seek the content inside the documents, just giving a chance to conduct a search by files names or attributes.

4) Document versions comparison is an important function, but quite often it’s unavailable due to its implementation complexity. If the document is not just a plain text, this task becomes even more complicated.

5) Automatic categorization. This operation turns to be burdensome, that’s why it seems logical for the company to convert the process into a manual one and find an expert in document categorization solutions who will deal with all the settings and adjustments of the document management system.

One of the solutions to the above mentioned problems may be the usage of intellectual tools to process documents, which gives a possibility to aggregate and extract knowledge out of the mass data. So, this approach will be the right fit for businesses without a solid document management workflow.



To cite an example, let’s have a closer look at such practical solutions.

Global document catalogs creation. The first step towards systematic corporate knowledge is the creation of a general catalog where the semantic document comparator and categorizer will provide the ability to:

  • Group duplicate documents or similar documents
  • Automatically categorize documents (make catalogs)

Companies can conduct such a process by creating predefined categories or by learning custom categories. The learning process is very easy – each user can set his/her own categories: suffice it to invent a suitable name and choose some reference documents, related to this category.  The next steps will be automatic: the documents stored in the database will be re-categorized and the new documents will be managed according to the new category.

Document comparison. Linguistic tools allow comparing documents in an efficient way under the principle:

  • One to one
  • One to many
  • Many to many (document groups comparison)

Another useful feature consists in tracking exact differences in the compared documents. The analysis is made at the semantic level, so the user is granted an opportunity to see differences and similarities of linguistic constructions, not just slight corrections or text formatting peculiarities.

Similar documents. Furthermore, linguistic tools made it available to extend search boundaries, allowing to use the full document as a search query.

Additional features

Taking into account the fact that all the corporate documents are included into the database semantic index, organizations can use additional knowledge management tools:

  • Question answering system. It’s kind of a search system that allows to find documents and answers to natural language questions that users send to the database
  • Search results clustering and filtering. Here we mean a sub-structure that makes it possible to effectively process search results thanks to the intelligent filter system
  • Sentiment analysis. This tool is ideal for tracking buyers’/clients’/employees’ feedback, detecting positive and negative opinions stored in a multitude of corporate documents


Thus, the usage of knowledge management tools certainly improves the efficiency of traditional document management systems and releases organizations from the necessity to implement a huge upgrade, which, in turn, allows to save time and funds on elaboration/integration processes and staff training.


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