Natural language processing intends to build systems that interpret and respond to text or voice data and respond with text or speech of their own.
Build custom text classification models with machine learning models. Using natural language processing, analyze the sentiment towards specific target phrases and text to extract metadata from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles using natural for advanced text analysis.
Various algorithms and computation techniques are used to recognize speech into text and improve its accuracy of transcription using statistical modeling systems with complex probability and mathematical functions to determine the most likely outcome.
Customised speech recognition models can be used along with acoustic models, a pronunciation dictionary, and language models to transcribe domain-specific terms and rare words by providing hints and improving transcription accuracy of specific words which powers virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms.
Generate textual output from structured data using machine learning and deep learning models with explicit control states for virtual assistants.
Capture context and provide intelligent responses by using natural language processing that helps chatbot simulate conversations with users through messaging applications.