Natural Language Processing
A Real Innovation In Business Communication
Language affects what we think, how we think, the way we learn and make decisions. But no longer are we the only ones to master the human language. AI’s subfield NLP focuses on making it possible for computers to read, understand, and generate human language, as well as allowing humans to interact with computers using natural language.
NLP Tasks & Use Cases
Out of the many ways in which NLP can be useful for business, these are the most popular and demanded.
Industries
Need more detail? See NLP practical applications for industries
Marketing
Analyze customer feedback and social media posts to understand customer sentiment and improve marketing strategies. Any provider of goods and services can improve their SEO and generate unlimited content using Chat GPT.
Finance
Derive information from news and media reports to generate predictions of financial market events and make informed decisions. Take the most of credit scoring, anomaly & fraud detection.
Legal
Go through legal documents and extract relevant information for use in legal research. Legal texts are staffed with links and references that are not clickable; with NLP, you’ll be able to identify those links and have them explained in the right context.
Education
Analyze student writing and provide feedback, as well as extract information from educational materials and create personalized learning experiences.
How we make it work
There are multiple techniques and approaches used in NLP. At a high level, here’s what we do to make the most use of natural language processing.
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Selecting proper architecture and base model
The latest NLP solutions are not trained from scratch. Instead, they rely on pre-trained networks, such as LayoutLM or ViT. High-quality language representations of pre-trained networks allow us to focus on the task we’re solving. Our expertise in pre-trained networks helps our clients use the most suitable model for their case and achieve top performance.
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Text Preprocessing
We clean and prepare the text data for further analysis. It can include such tasks as lowercasing, tokenization (splitting the text into individual words or phrases), and removing punctuation or stop words (common words that don't add much meaning to the text).
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Feature Extraction
At this step, we extract meaningful features or characteristics from the text data. These features can be the presence of certain words or phrases, the part of speech of each word, or the overall sentiment of the text.
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Model Training
Training requires using machine learning algorithms to train a model on a large dataset of labeled text data. Once trained, the model can make predictions about new, unseen text.
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Model Deployment
Finally, we deploy a trained and evaluated model in a real-world application, such as a chatbot or language translation tool.
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Model Evaluation
Training requires using machine learning algorithms to train a model on a large dataset of labeled text data. Once trained, the model can make predictions about new, unseen text.
1
Text Preprocessing
We clean and prepare the text data for further analysis. It can include such tasks as lowercasing, tokenization (splitting the text into individual words or phrases), and removing punctuation or stop words (common words that don't add much meaning to the text).
2
Feature Extraction
At this step, we extract meaningful features or characteristics from the text data. These features can be the presence of certain words or phrases, the part of speech of each word, or the overall sentiment of the text.
3
Model Training
Training requires using machine learning algorithms to train a model on a large dataset of labeled text data. Once trained, the model can make predictions about new, unseen text.
4
Model Evaluation
Training requires using machine learning algorithms to train a model on a large dataset of labeled text data. Once trained, the model can make predictions about new, unseen text.
5
Model Deployment
Finally, we deploy a trained and evaluated model in a real-world application, such as a chatbot or language translation tool.
Challenges
NLP challenges and how we overcome them
Out of the many ways in which NLP can be useful for business, these are the most popular and demanded.
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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 challenge is English being miles ahead of the curve when it comes to technological advancement.
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.
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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.
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.
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Need for annotation
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.
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.
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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.
Building domain-specific models which are trained on data particularly to the desired domain can help overcome this challenge.
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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.
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.
FAQ
Frequently Asked Questions
How is NLP used in business?
How much does it cost to develop an NLP system?