Success Metrics - Stefanini

Success Metrics

Ensuring NLP Models Work Correctly

Every business works to provide services and products to customers promptly and effectively. Success metrics evaluate general text and machine translation tasks.

Organizations need metrics to solve multiple problems and determine whether an NLP model works well.  To explain a model, one needs the right metric for ML and NLP.

NLP Capabilities

Virtual Assistants For Different Industries

Access the benefits of scalable virtual administrative assistance and enable your workforce to focus on their expertise by connecting to personnel with specialized industry knowledge.

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Sentimental And Semantic Analysis

Our NLP system captures high-level semantics and defines emotional values in order provide appropriate and meaningful responses, leading to higher quality customer interactions.

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Information Retrieval & Clustering

Automatically develops structured text data and label clusters that enable SAI to retrieve information swiftly and answer verbal user queries with precise responses.

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Voice Enabled Applications

Leveraging NLP and Machine Learning (ML), we develop application functions that respond to user voice commands, automating administrative activates, data retrieval and more.

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Deploying and developing sophisticated NLP applications is a critical factor in business. NLP evaluation metrics are crucial to measuring the ROI — what’s working, what’s not and where to improve.

Creating metrics will improve the enterprise model. Benchmark metric processors such as General Language Understand Evaluation (GLUE), Stanford Question Answering Dataset (SQuAD) are backdrops to improving NLP models.

Enterprise NLP applications need customized metrics per use case to measure model success.

NLP accuracy is measured with two specific performance metrics: precision and recall.

Use cases for measuring NLP success include:

  • Topic extraction and insights. Extracting common themes and topics from datasets gives marketers a sense of what their audience is thinking. Measuring the performance enables the NLP model to work better.
  • Streamline business operations. NLP is valuable for employee productivity and simplifying critical business operations. Ensuring the NLP model is helping business operations is achieved through measurement.
  • Sentiment. Understanding feedback, opinions and emotions through written and spoken languages provides insights into how users feel.
  • Text analysis. Measuring how an NLP model is breaking down words in documents is crucial to ensuring the model is working correctly.

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