In newspaper articles, emails, and other texts, it is natural to reference dates in relation to the publication date. For example, you will commonly see phrases like “yesterday” and “two weeks ago.” However, if you read an article a year after it was published, these timeframes are not immediately clear. When analyzing articles using natural language processing (NLP) techniques, relative dates referenced in an article will not be interpreted properly, if at all, since there is no context for the models. Thus, in order to effectively use tools such as event extraction and question-answering engines, relative references need to be…
State-of-the-art NLP made easy with AdaptNLP
Natural Language Processing (NLP) has advanced significantly since 2018, when ULMFiT and Google’s release of the BERT language model approached human-level performance on a range of use cases. Since then, several models, such as XLM, GPT-2, XLNet, and ALBERT, have been released in quick succession, each improving on its predecessors. While these state-of-the-art models can solve human-level, language-based tasks on large volumes of unstructured text for certain use cases, getting a handle on what to use, when to use it, and how to use it can be a challenge. At Novetta, we explored what it would take to streamline the…
NovettaNLP
A high-level framework for testing, running, and deploying state-of-the-art natural language processing models. NovettaNLP provides access to cutting-edge natural language processing (NLP) capabilities through a standardized API, empowering users to perform named entity recognition, text classification, and question answering. By standardizing the input/output data and function calls, regardless of the model used in the backend, NovettaNLP simplifies implementation of NLP algorithms. New, high-performing models are integrated seamlessly into NovettaNLP, with all functionality made available through an easy-to-use, production-ready python package. Click image to download.
NovettaNLP
A Powerful, Transformers-Based Framework for End-to-End Natural Language Processing Tasks
BERT’s release in November 2018 marked an inflection point for Natural Language Processing (NLP), moving us closer to human-level performance on a range of use cases. Since then, many transformer-based language models, such as XLM, GPT-2, XLNet, and roBERTa, have been released, with each subsequent model raising the performance ceiling. With the availability of these state-of-the-art pre-trained language models, we can produce NLP models that solve a variety of downstream language-based tasks, enabling our customers to quickly analyze large volumes of unstructured text. The challenge with using these models is that they each differ slightly in their application. For example,…
Deploying Advanced Machine Learning Models at Scale
Novetta is using deep learning to automate advanced analytic pipelines for our customers. These new capabilities identify important pieces of information across large text, image, and video data sets, a challenge faced by many of our Defense and Intelligence Community customers. We developed Argo, a standardized, Docker-based model training and deployment pipeline, to enable rapid evaluation and deployment of state-of-the-art deep learning models. Argo enables models to be deployed as endpoints and, when used with Amazon SageMaker, it forms the foundation for us to deploy, scale, and manage our deep learning models. Simplifying infrastructure setup through Docker and AWS allows…
State of the Art Text Classification with ULMFiT
Novetta’s Matt Teschke spoke at ODSC East – Boston in the session,”State of the Art Text Classification with ULMFiT” on May 2, 2019. The rise of the internet has led to a faster flow of information, where news posted to a relatively obscure blog can be shared on social media and reach national publications within hours. The volume of information is such that humans alone cannot filter out noise, identify important new viewpoints, and determine how messaging trends are changing over time. At Novetta, we are constantly evaluating advances in deep learning to help our customers…
