Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks the availability of large corpora collections and capability deep architectures shape internal mechanisms in self-supervised processes (also known as “pre-training”), versatile performing models released continuously for every new network design. These networks, some...

Spark NLP is a Natural Language Processing (NLP) library built on top of Apache ML. It provides simple, performant & accurate annotations for machine learning pipelines that can scale easily in distributed environment. comes with 1100+ pretrained and models more than 192+ languages. supports nearly all the tasks modules be used seamlessly cluster. Downloaded 2.7 million times experiencing 9x gr...

In this paper, we describe the Lithium Natural Language Processing (NLP) system a resource-constrained, highthroughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currentl...

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, b...

We present Kathaa, an Open Source web-based Visual Programming Framework for Natural Language Processing (NLP) Systems. Kathaa supports the design, execution and analysis of complex NLP systems by visually connecting NLP components from an easily extensible Module Library. It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in the...

NLP track has been organized for the first time at TREC-5 to provide a more focused look at how NLP techniques can help in achieving better performance in information retrieval. The intent was to see if NLP techniques available today are mature enough to have an impact on IR, specifically if and when they can offer an advantage over purely quantitative methods. This was also a place to try some...