Every company that derives value from language stands to benefit from NLP, the branch of machine learning that has the most transformative potential. Language is the lowest common denominator in almost all of our interactions, and the ways in which we can capture value from language has changed dramatically over the last three years. Recent advancements in NLP have outsized potential to accelerate business performance. It even has the promise of bringing trust and integrity back to our online interactions. Large incumbents have been the first to jump onboard, but the real promise lies in the next wave of NLP applications and tools that will translate the hype around artificial intelligence from ideology into reality. So, there you have it, these are my personal highlights of 2021 in NLP. I hope you enjoyed this summary and it'd be great to hear about your personal highlights from the past 12 months in NLP. Please comment on this blog post or reach out directly.
The advent of Transformers in 2017 completely changed the world of neural networks. Ever since, the core concept of Transformers has been remixed, repackaged, and rebundled in several models. The results have surpassed the state of the art in several machine learning benchmarks. In fact, currently all top benchmarks in the field of natural language processing are dominated by Transformer-based models. Some of the Transformer-family models are BERT, ALBERT, and the GPT series of models.
Text Summarization is highly useful in today's digital world. I will now walk you through some important methods to implement Text Summarization. This is the traditional method, in which the process is to identify significant phrases/sentences of the text corpus and include them in the summary. The summary obtained from this method will contain the key-sentences of the original text corpus. It can be done through many methods, I will show you using gensim and spacy.
Let's say that you have an understanding of how to tackle natural language processing tasks. Let's also say that you have decided, more specifically, the type of approach you will employ in attempting to solve your task. You still need to put your plan into action, computationally, and there is a good chance you will be looking to leverage an existing NLP library to help you do so. Assuming you are programming in Python (I can't help you if not), there is quite a landscape of options to choose from. While this article is not an endorsement of any particular collection of such solutions, it serves as an overview to a curated list of 5 popular libraries you may look to in order to work on your problems.
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.