Part 8. Semantic Roles — In this tutorial of Codeq’s NLP API we will focus on a single module that can be used to extract semantic roles from texts. Previous tutorials of this series can be found here: Part 1. Getting started and sending requests to the API. Part 2. Calling NLP annotators for linguistic…

Part 7. Summarization — Codeq’s NLP API includes text summarization modules that can help you to identify the most relevant content from texts. In this tutorial we will detail how to use modules related to text summarization, sentence compression and extraction of keyphrases. Previous tutorials can be found here: Part 1. Getting started and…

Part 6. Speech Acts, Questions & Tasks — In this tutorial we will detail modules from Codeq’s NLP API that can be used to classify speech acts and questions, and detect tasks from texts. Previous content of this series of tutorials can be found here: Part 1. Getting started and sending requests to the API. Part 2. Calling…

Part 5. Named Entities — We started this series of tutorials to show you how to call different modules of Codeq’s NLP API. So far, we have covered the following topics: Getting started and sending requests to the API. Calling NLP annotators for linguistic analysis. Using text classifiers to classify sentiments and emotions, and detect…

Part 4. Abuse classifier — In this tutorial we showcase the most recent module from Codeq’s NLP API, which identifies abusive and harmful content from texts. This is a follow up tutorial; previous content can be found here: The first tutorial covered how to get started and send requests using our Python SDK. In the…

Part 3. Semantic features — In this tutorial we describe how you can call some of the text classifiers of our NLP API; specifically, we showcase modules that are focused on understanding the intrinsic semantics and pragmatics of texts. This is a follow up tutorial; previous content can be found here: The first tutorial covered…

Part 2. Linguistic features — We started this series of posts to explain how you can call our NLP API to analyze your own documents. The first tutorial covered how to get started and send requests using our Python SDK. In this second tutorial we will show you different modules that can be applied to…

Part 1. Getting started and making requests — In this series of posts we want to showcase different examples on how to call our NLP API to analyze your own texts. Getting Started To use our NLP API you need to sign up to generate a USER_ID and USER_KEY. …

An exploratory analysis showing how Semantic Roles can be used to extract Knowledge-Rich Contexts. See it in action here: https://labs.codeq.com/semantic_roles_cord_19 One of the most recent modules we have developed at Codeq NLP API is the Semantic Role Labeler. The goal of this module is to understand the underlying semantics of…