Natural Language Processing deals with how to recognize patterns in natural, unstructured text. Think of structured text as data in a database or excel table, for instance a register of names. By unstructured information we mean text in emails, documents, manuals etc. The term natural highlights that the text data has been generated by a human for another human.
Home /
NLP
Natural Language Processing
1
Process
2
Algorithm
3
Decision
The last years have seen tremendous improvements with regards to the quality of pattern recognition in unstructured data. The reason for this is next to hardware improvements mainly a group of algorithms, which go by the name of neural nets or deep learning. A key feature of these approaches is that given enough training data, they form their own set of rules in order to achieve a certain goal. This way millions of implicit rules may be defined to successfully recognize even rather complex patterns.
Criteria to discover attractive process automation projects, where visual information play a crucial role:
- Currently the process is cost-intensive and/or a faster decision creates substantial value
- A (trained) human could make a good decision mainly based on text
- There is enough data available (as a rule of thumb: 500 - 10.000 documents. This, of course, is highly dependent on the use-case)
In our experience, only by combining knowhow of internal operations with natural language processing expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.
Case Study
Natural Language Processing
LEGAL REVIEW OF RENTAL CONTRACTS
Our client is an online tenant protection club that offers its clients the legal review of their tenancy agreements by experienced partner lawyers. The aim of the project is to develop an intelligent software tool that supports... read more
Blog Posts regarding NLP
August 12th, 2019
Natural Language Processing (short: NLP, sometimes also called Computational Linguistics) is one of the fields which has undergone a revolution since methods from... read more
April 23rd, 2019
There is a growing demand for automatically processing letters and other documents. Powered by machine learning, modern OCR (optical character recognition) methods can digitize the text. But... read more
Use Cases in Natural Language Processing
Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.
Electronic patient records can be evaluated using machine learning algorithms. This automatically provides doctors with suggestions for diagnoses and therapy options.
Companies working with thousands of customers and suppliers need to categorize their documents so that requests can be handled in time.
Standard documents can be classified and automatically completed using voice input and machine learning algorithms. This document can be checked and edited afterwards.
Construction plans are complex and include a high amount of relevant data. This information, however, is not standardized and available in a structured format. Therefore, data cannot be analyzed accurately and construction projects cannot be compared on a profound basis.
Companies receive dozens of invoices every day. Handling invoices and ensuring payment deadlines are met is a central task at every accounting division.
Rental contracts are often very complex and have dozen of pages. However, for some tasks, like in a Due Diligence process, only some information is important.
Machine learning algorithms allow standard clauses and deviations from the standard to be defined and identified on the basis of historical contracts. This makes it possible to highlight deviations and identify and categorize contracts according to predefined criteria.