Natural Language Processing

All languages are different

There are many different languages in the world, each with its own unique structure, grammar, vocabulary, and syntax. Even more importantly, different languages are trained on different text corpora, and the performance of resulting NLP models is very different. A related challen

Our solution:

Don’t worry if your task requires different languages. Our team has experience working with different languages. No matter whether it is Latvian, Danish, or Turkish — we will choose the strongest pre-trained model for your case and fine-tune it for your task to get the maximum performance possible.

Meaning depends on context

Natural language is often ambiguous, meaning the same words or phrases can have multiple meanings depending on context. This can make it challenging for NLP systems to understand the intended meaning of a given text. Preprocessing the data, including tasks like tokenization, stemming, and lemmatization can help extract relevant information and make it more suitable for NLP tasks.

Our solution:

We train ML model to make your chatbot smarter, so it’s always one step ahead.Using state-of-the-art pre-trained models lets us utilize textual representations of the highest quality. Such representations of texts allow us to effectively understand the semantic meaning of the texts and solve various tasks.

Need for annotation

Generative AI creates dynamic, open-ended responses that sound human, not scripted.To develop and train NLP systems, large amounts of annotated data (labeled or marked-up data to indicate relevant information) can be needed. Annotation is often time-consuming and labor-intensive.

Our solution:

Sometimes it is possible to solve tasks just by just choosing the right pre-trained model without having a large annotated dataset. NLP can use the unsupervised learning approach, which means using machine learning algorithms to analyze and cluster unlabeled datasets. Such algorithms identify hidden patterns or data groupings without human intervention needed.

Domain-specific knowledge

NLP systems often need to be fine-tuned or adapted to work well in specific domains. This can be challenging due to the need for additional data specific to the desired domain and the varying characteristics of language between different domains.

Our solution:

Building domain-specific models which are trained on data particularly to the desired domain can help overcome this challenge.

Model underperformance

Sometimes the model does not achieve the desired results during training. This can be due to data consistency, quality, or quantity not being enough.

Our solution:

To enhance the NLP model accuracy, various techniques are applied: data augmentation, distillation from the larger model, feature engineering, and feature selection — these and more can upgrade your model.