In order to increase the search engine relevance of text extracts, we are investigating the paraphrasing competence of the language model ChatGPT for one of our customers. The goal is to automatically reduce full texts (text summarization) and to generate text snippets using paraphrasing that are evaluated by the search engine as “unique” as possible. By optimizing the textual uniqueness, the snippets should be ranked as high as possible in the hit list in the web search and thus lead to a better conversion.
For the implementation of this project, LangTec specifically designed and operationalized a measure to quantify textual uniqueness. In this project, LangTec thus also contributes its computational linguistics expertise and many years of experience in the automated generation of texts.

For a provider in the field of recruiting, we successfully implemented the generation of sample job advertisements using the large-scale language model ChatGPT. The texts generated with our solution serve recruiting companies in the creation of their job advertisements as a starting point for further individual adaptations.
For this purpose, LangTec developed a web application as well as suitable instructions, so-called “prompts,” for the language model of OpenAI. The prompts also included textually relevant information from a domain-specific database, which should be considered by ChatGPT in the output.
After having implemented extensive text generation solutions based on template-based text generation (NLG) over many years in the past with our in-house developed solution TextWriter, we are now happy to use generative language models in our text generation projects with this project.

To celebrate LangTec’s 12th anniversary, we met for brunch today at Pynk Coffee, a cozy café close to the office. While we tasted delicious macarons, croissants and cakes, we reminisced about past projects and also shared the excitement about upcoming projects. Accompanied by the one or the other cup of coffee or tea, we really enjoyed ourselves..

Our client is aiming to prequalify suitable investment targets. In a yet another project involving large transformer-based language models, LangTec has developed a solution to identify all relevant company types based on company website information. The key challenge in this task is to deal with huge amounts of website content whose length exceeds the typical sequence length limitation posed by transformer-based language models. LangTec’s solution was optimised for recall, i.e., it was designd to capture all potentially interesting companies in the training and test set.
In addition to designing, training and optimising the perfect-recall classifier, LangTec successfully trained a hybrid language model that uses features from a another, non-neural-network statistical model along with features from the transformer-based model to arrive at a joint classification decision. This model architecture permits to combine transformer-based models with other machine-learning models in a hybrid architecture.

In a new project, LangTec gets to shine with its expertise in the areas of Data Science, Machine Learning and Big Data – and this time, actually quite directly. We are pleased to have been awarded a new contract to create comprehensive training materials for an advanced training program for adults. Specifically, we are designing documents for the modules Data Science, Machine Learning and Big Data.

We would be very happy to make our expertise in these areas available to anyone interested in gaining a foothold in this exciting field and learning advanced techniques.

We celebrated St. Nicholas Day with plenty of chocolate!

We have not managed the whole kilogram, but fortunately the Christmas season is not yet over…. 🙂

Big congratulations to Lang.Tec on winning the public tender of a large German tourism project! Part of the project will involve improving and extending an open data Knowledge Graph. The additional information will be obtained by extraction from unstructured description texts on various entities such as hotels, events, restaurants, tours and others.

To accomplish this, Lang.Tec will employ state-of-the-art natural language processing (NLP) techniques to extract touristically relevant information and add it in structured form to the Knowledge Graph. This will permit the associated chatbot to access a wealth of additional structured information, which will help it provide more useful and accurate answers to users.

In addition to improving the Knowledge Graph, Lang.Tec will also develop a machine-learning-based module to answer difficult user questions. This module will support the chatbot in understanding context and intent of users’ questions, thus enabling it to provide more relevant and helpful answers for hard questions.

Overall, Lang.Tec’s work on the German Touristic project will help improve the data-driven capabilities of the chatbot, making it a more valuable resource for travellers and tourism professionals in Germany. We very much look forward to experiencing the positive impact this will have on the chatbot user experience.

In November, our new working student Kilian joined the LangTec-Team!

At LangTec, Kilian has the opportunity to apply and further develop the knowledge he is gaining being enrolled in the master’s degree ‘Data Science & Artificial Intelligence’ at the University of Applied Sciences in Wedel. Currently, in the context of a project in which we are developing training materials for an adult education program and next year also in the context of the master thesis.

We look forward to working with you and wish you a good start at LangTec!

We are delighted to welcome our new colleague Alicia to the LangTec-team. Alicia holds a PhD in computational linguistics and brings a strong background in Arab studies and Digital Humanities to the table. Over the past ten years she more and more gravitated towards software engineering. With her many years of applied experience in a range of different project contexts Alicia now joins our software development team in a senior role.

We are delighted to announce the start of a new project with a well-known major social network. Its multilingual ontology, which is a central element in many of their internal services, has been curated manually so far and has grown continually over the years. Now it is time for a critical analysis of the status quo of this knowledge representation and to develop suggestions for the future curation and further development of said ontology. In this LangTec assists with its expertise in ontologies, knowledge graphs, computational linguistics and language technology.

We visited the client on-site for the official project kick-off and were warmly welcomed despite some rather windy autumn weather outside.