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Natural Language Processing

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.

Criteria to discover NLP projects

Criteria to discover attractive process automation projects, where visual information plays 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)

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. In our experience, only by combining know how 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.

Our process

1. Process evaluation

Together we discuss your process automation projects along three different dimensions: cost savings, strategic value and technical feasibility. After settling for a specific project, we put special emphasis on the needs of the end users.

2. Innovative solutions

We are an experienced team of machine learners. Our algorithms find complicated patterns in unstructured, mostly visual and text data. Once detected, these patterns are the basis for the automation of the underlying process.

3. Decision-support software

We make a point of integrating our customers in the project's code repository as well as in weekly progress meetings. Agility, clean code and a modular program structure help us to deliver easy-to-maintain software, that simply works.

Projects in Natural Language Processing

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Legal Review of Rental Contracts

Through dida's expertise in the field of natural language processing (NLP) we succeeded in creating a software for the legal review of rental agreements.

Automatic Checking of Service Charge Statements

With machine learning and NLP: Read about the development of software for the automatic verification of settlements using artificial intelligence.

Semantic Search for Public Administration

Machine learning and information extraction: dida's AI-based algorithm simplifies business registrations through intelligent semantic search.

Numeric Attribute Extraction from Product Descriptions

Machine learning & natural language processing (NLP) for online platforms: Development of a software for information extraction from product descriptions for Idealo.

Extracting information from customer requests

As a machine learning service provider, we used natural language processing (NLP) to make software for extracting information from customer requests.

Blog Posts in Natural Language Processing

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Natural Language Processing

Latest developments in the world of Natural Language Processing: A comparison of different language models

By Justus Tschötsch May 24th, 2023

Read about the latest developments in the world of natural language processing (NLP) and a comparison of different language models here.


How ChatGPT is fine-tuned using Reinforcement Learning

By Thanh Long Phan April 11th, 2023

In this blog post, we present an overview of the training process of ChatGPT and have a closer look at the use of Reinforcement Learning in language modeling.


Recommendation systems - an overview

By Konrad Mundinger August 29th, 2022

Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.

Computer Vision

Image Captioning with Attention

By Madina Kasymova May 31st, 2022

An application of deep learning: Read here about image labeling algorithms & an approach to image labeling - the attention mechanism.

Natural Language Processing

OpenAI Codex: Why the revolution is still missing

By Fabian Gringel February 18th, 2022

Learn more about how OpenAI's codex language model works and how it differs from GPT-3. Codex software explanation and experiences here.


Ethics in Natural Language Processing

By Marty Oelschläger (PhD) December 20th, 2021

Learn more about the ethics in natural language processing (NLP), the societal impact of machine learning (ML) & why caution should be exercised.

Natural Language Processing

GPT-3 and beyond - Part 2: Shortcomings and remedies

By Fabian Gringel October 24th, 2021

Expand your knowledge about GPT-3 and read here about opportunities, weaknesses & troubleshooting as well as alternatives of the AI-based language model.

Computer Vision

Data-centric Machine Learning: Making customized ML solutions production-ready

By David Berscheid October 6th, 2021

Read here about model- and data-centric machine learning & how we at dida improve machine learning projects by using data-centric techniques.

Use Cases in Natural Language Processing

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Webinars in Natural Language Processing

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