Natural Language Processing (NLP) is a field of artificial intelligence that teaches computers to understand and generate human language. Powerful applications range from understanding and automatically answering customer queries to automated content moderation tools that can identify and remove harmful or inappropriate language.
As we continue to explore the unlimited potential of NLP, we already have some innovative solutions in store:
Semantic search improves search accuracy by analyzing the relationships between words and concepts to deliver search results that better match the user's intent, rather than just matching keywords.
Text classification and clustering automatically categorize text based on its content, such as news articles or customer feedback, into predefined categories like topics or sentiment for better analysis and organization.
Pseudonymisation replaces personal data with pseudonyms to protect privacy and comply with data protection regulations like GDPR. This allows the use of data for NLP tasks while reducing the risk of re-identification.
Sentiment analysis involves automatically determining the sentiment of a piece of text, such as whether it is positive, negative, or neutral. This can be used to analyze customer feedback, social media posts, or product reviews.
Document processing is the conversion of analog documents into digital format, which involves analyzing the layout, extracting information, and creating digital images for archiving or further use.
Text generation uses large language models to create new and understandable natural language output, such as weather reports, patient reports, image captions, or chatbots.
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Thanks to our linguistics and computer science expertise, we’re able to overcome functional and technical challenges NLP challenges such as:
Natural language is subjective and ambiguous, which makes it difficult for machines to accurately process language due to multiple meanings that can depend on the context and speaker.
Machine learning models, including NLP, need large amounts of high-quality data, such as diverse, well-labeled, and clean data, to reduce bias and increase accuracy and reliability.
NLP models are complex and require technical expertise and computational resources to tune many layers and parameters for optimal performance.
NLP systems balance accuracy and speed. This means that more complex models are usually more accurate but slower, while simpler models are faster but less accurate.
The first step in using NLP is to define the problem that needs to be solved. This involves identifying the data to be analyzed, the questions to be answered, or the business objectives to be met.
After defining the problem, data needs to be collected and prepared for analysis or model training. This involves tasks such as tokenization, stemming, and removing stopwords.
Choose the appropriate NLP technique for the specific problem, such as sentiment analysis, topic modelling, or entity recognition.
Train model(s) on data using machine learning algorithms like Naive Bayes, Support Vector Machines, or Transformers depending on the problem. Not all solutions require a custom trained model.
Test and evaluate the performance of the chosen NLP technique(s) by measuring metrics such as accuracy, precision, and recall to determine how well the approach is working.
Based on the client's requirements, the deployment of the NLP solution must be optimized for cost and inference time. Continuous monitoring is needed to detect performance changes that call for action.
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