Showing results for Natural Language Processing - ISE Developer Blog

Movies and TV shows require multiple takes per scene and may have a substantial amount of archival footage as a result. Here, we use Azure Cognitive Services and custom code to develop a multi-model Machine Learning (ML) solution to automatically detect discardable footage to save media companies manual archiving hours and storage space.

In this blog post we cover the process, requirements, and the design of an evaluation framework for NLP and Information Extraction. We cover the reasoning behind such a framework, and discuss its implementation with examples from a Named Entity Recognition evaluation point of view.

This code story describes a collaboration with ZenCity around detecting trending topics at scale. We discuss the datasets, data preparation, models used and the deployment story for this scenario.

We analyze how Hierarchical Attention Neural Networks could be helpful with malware detection and classification scenarios, demonstrating the usefulness of this approach for generic sequence intent analysis. The novelty of our approach is in applying techniques that are used to discover structure in a narrative text to data that describes the behavior of executables.

The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consuming simple HTTP services for language identification, tokenization, part-of-speech-tagging and named entity recognition.

Using Azure Functions and the open-source JavaScript library Compromise for inferring location in social media posts.

Using Microsoft Bot Framework's QnA Maker to create a conversational interface for publishing FAQ-style content.

An an end to end smart conversational client, that enables low latency voice and text interaction, using Microsoft's intent recognition technologies and a local smart cache.

An examination of whether a more sophisticated learner will always result in better performance in a text-based classifier, and the trade-off between accuracy and training time.