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31.08.2021 | News NLP means searching without problems

A lot of people are unaware of how much time they waste searching for documents in their working lives. The combination of AI-based search and analysis software with Natural Language Processing is here a real efficiency booster. IntraFind Software AG presents three efficiency-boosting use cases in the enterprise sector.

Natural Language Processing (NLP), is based on many linguistic and syntactic procedures as well as machine learning methods. It comes into its own in everyday professional life in particular, saving not only a great deal of time but also a lot of money. How attractive the broad field of digital language processing really is demonstrates enterprise search specialist IntraFind by using three practical applications from the corporate sector.

1. Intelligent natural language and dialog-based search. Natural language search input in enterprise search systems is a prime example of NLP usage. Questions such as "Which business partner brought in the most revenue in 2020?" already deliver results that would otherwise only emerge through tedious manual evaluation of multiple search queries. Compared with keyword searches, the user gets the desired information faster and more intuitively. Many companies offer natural language and dialog-based search via chatbots, especially in customer support. Virtual assistants can record queries, compare them with FAQ databases and answer them.

2. Text classification and keywording of documents. NLP methods are especially useful in applications whose purpose is the evaluation (classification) of texts. Natural Language Processing automates the process by automatically detecting urgency, mood (sentiment) or content (technical problem, termination, billing issues). In the next step, the program usually generates an appropriate response or forwards the query to an appropriate expert in the company. A practical example of this is the automatic sorting of incoming mail. The potential for optimization lies primarily in companies where manual presorting of incoming texts for further processing plays a major role. Many departments have collective mailboxes, the contents of which NLP-supported software can classify without further human intervention. This process is of course also applicable to letters or faxes that the mailroom digitizes via OCR (Optical Character Recognition).

3. Analysis of large volumes of text. The content analysis of large document collections is also easier with an NLP solution. Using linguistic rules, artificial intelligence and machine learning, such software extracts the essentials from hundreds of multi-page texts. Lawyers who deal with changes in the law or company takeovers, for example, can evaluate large volumes of contracts with ease. Thanks to Natural Language Processing, the program automatically recognizes important data points and clauses and delivers them to the user for targeted viewing, commenting and editing. The time saved is enormous and the lawyer can once again devote himself to the essential tasks of his profession, such as checking the legal validity of the contracts evaluated in this way. This also limits the risk of overlooking relevant clauses.

"The practicality of Natural Language Processing is already evident today," says Franz Kögl, CEO of IntraFind Software AG. "NLP software minimizes the often high amount of time that employees spend on information searches that are often completely inconclusive. Users can ask complex, natural language questions and get much better search results. As shown, the range of applications is very versatile: Whether internet or enterprise search, whether knowledge management solution or digital workplace, Natural Language Processing significantly enhances search-driven applications. But NLP can also be very helpful in the area of user helpdesks, self-services and chatbots."

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