Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. NLP is one of the hottest fields in AI.

Today, NLP is broadly adopted by businesses across industries in several forms. In fact, according to recent research, the global NLP market size is expected to reach $35.1 billion by 2026. This ubiquity of the technology form can be accorded to the abundance of text and voice data as well as the shift from human-computer interaction to human-computer conversation. In my upcoming talk at the Open Data Science Conference (ODSC) East, I am excited to be sharing my thoughts on how NLP is already aiding businesses, trends to keep an eye out for in the near future, and things to keep in mind when it comes to adopting NLP solutions. Outlined below is what you can expect me to discuss in detail during the presentation.

Part of Speech (POS) Tagging is an integral part of Natural Language Processing (NLP). The first step in most state of the art NLP pipelines is tokenization. Tokenization is the separating of text into "tokens". Tokens are generally regarded as individual pieces of languages – words, whitespace, and punctuation. Once we tokenize our text we can tag it with the part of speech, note that this article only covers the details of part of speech tagging for English.

Part of Speech (POS) is a way to describe the grammatical function of a word. In Natural Language Processing (NLP), POS is an essential building block of language models and interpreting text. While POS tags are used in higher-level functions of NLP, it's important to understand them on their own, and it's possible to leverage them for useful purposes in your text analysis. There are eight (sometimes nine) different parts of speech in English that are commonly defined. Noun: A noun is the name of a person, place, thing, or idea.

LEWES, Del., Oct. 05, 2021 (GLOBE NEWSWIRE) -- John Snow Labs, the Healthcare AI and NLP company and developer of the Spark NLP library, today announced that it will enable free access to its enterprise-grade Annotation Lab and NLP Server software for all users. This announcement comes on the first day of the company's annual NLP Summit, a free online event that brings together the AI community to discuss the most important trends, use cases, and solutions advancing natural language processing (NLP). The Annotation Lab, a robust data labeling and AI/ML solution for teams, enables users to annotate documents, images, and videos. The software automatically trains models using active learning and transfer learning. The simple and efficient project-based workflow helps users leverage real-time analytics on productivity, dataset bias, inter-annotator agreement, and more.

To become an Artificial Intelligence Engineer, you must meet the minimum criteria described in this article. But first, you must know if this is the career for you. It can be seen that the global artificial intelligence market is experiencing a huge growth of 154%. So what sparked this revolution? There were 3 main factors influencing this exponential growth.

While visual'no code' tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools -- at the'deep tech' AI coal face -- still requires some expert help (and/or costly in-house expertise). This is where bootstrapping French startup, NLPCloud.io, is plying a trade in MLOps/AIOps -- or'compute platform as a service' (being as it runs the queries on its own servers) -- with a focus on natural language processing (NLP), as its name suggests. Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP -- a technology that can help businesses scale their capacity to intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. OpenAI built a text generator so good, it's considered too dangerous to release Production ready (pre-trained) NLP models for English are readily available'out of the box'. There are also dedicated open source frameworks offering help with training models.

Natural Language Processing has been one of the most researched fields in deep learning in 2020, mostly due to its rising popularity, future potential, and support for a wide variety of applications. If you have played around with deep learning before, you probably know conventional deep learning frameworks such as Tensorflow, Keras, and Pytorch. Assuming that you know these basic frameworks, this tutorial is dedicated to briefly guide you with other useful NLP libraries that you can learn and use in 2020. Depending on what you want to do, you might be able to take away a few names of the tools that interest you or didn't know exist!

Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status detection, entity resolution, relation extraction, and de-identification. Reimplementing a Bi-LSTM-CNN-Char deep learning architecture on top of Apache Spark, we present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks without using heavy contextual embeddings like BERT. This includes improving BC4CHEMD to 93.72% (4.1% gain), Species800 to 80.91% (4.6% gain), and JNLPBA to 81.29% (5.2% gain). In addition, this model is freely available within a production-grade code base as part of the open-source Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java; and can be extended to support other human languages with no code changes.

The business value of natural language processing (NLP) is indisputable, and there's never been a time this technology has proven to be so useful. Just think: in the rapid shift to remote work in response to the global coronavirus pandemic, companies have leveraged NLP for everything from chatbots used to effectively onboard workers remotely, to safely interfacing with patients in healthcare settings. It's especially encouraging to see that, despite IT budgets being on a downturn (Gartner), enterprise leaders have not shied away from NLP investments. In fact, according to new research, survey respondents across industries, company sizes, and geographic locations reported increases in their organization's NLP technology budgets from 10-30% more compared to last year. With the proliferation of NLP services in the cloud, companies need not even install and manage open source NLP libraries.