The individual sentences are mostly grammatically accurate and can be comprehended by us. The connection between sentences however is where it needs to improve. The writing does not always flow naturally from one paragraph to the next. The AI seems to only try to define what NLP is. It did try to elaborate on some occasions and move on to different areas of an NLP blog but was not very clear about what it meant. As it stands, this particular model needs to improve on understanding the context of writing a blog post on a higher level such as getting into some more detail of the applications of NLP, or how it works under the hood, or perhaps even featuring a blog generated by AI. Current innovations include using computer vision in conjunction with NLP to simulate a more complete form of perception. For instance, if we have an image of cats in a cup, that is recognized by computer vision, and we ask for images with cats minus the cup, the AI can return images of just cats. Or if we wanted images of cats minus the cup, plus boxes, the AI can return images of cats inside boxes instead of cups, all without having these pictures manually tagged.
Editor's Note: This deep dive companion to our high-level FAQ piece is a 30-minute read so get comfortable! You'll learn the backstory and nuances of BERT's evolution, how the algorithm works to improve human language understanding for machines and what it means for SEO and the work we do every day. If you have been keeping an eye on Twitter SEO over the past week you'll have likely noticed an uptick in the number of gifs and images featuring the character Bert (and sometimes Ernie) from Sesame Street. This is because, last week Google announced an imminent algorithmic update would be rolling out, impacting 10% of queries in search results, and also affect featured snippet results in countries where they were present; which is not trivial. The update is named Google BERT (Hence the Sesame Street connection – and the gifs). Google describes BERT as the largest change to its search system since the company introduced RankBrain, almost five years ago, and probably one of the largest changes in search ever. The news of BERT's arrival and its impending impact has caused a stir in the SEO community, along with some confusion as to what BERT does, and what it means for the industry overall. With this in mind, let's take a look at what BERT is, BERT's background, the need for BERT and the challenges it aims to resolve, the current situation (i.e. The BERT backstory How search engines learn language Problems with language learning methods How BERT improves search engine language understanding What does BERT mean for SEO? BERT is a technologically ground-breaking natural language processing model/framework which has taken the machine learning world by storm since its release as an academic research paper. The research paper is entitled BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al, 2018). Following paper publication Google AI Research team announced BERT as an open source contribution. A year later, Google announced a Google BERT algorithmic update rolling out in production search. Google linked the BERT algorithmic update to the BERT research paper, emphasizing BERT's importance for contextual language understanding in content and queries, and therefore intent, particularly for conversational search. BERT is described as a pre-trained deep learning natural language framework that has given state-of-the-art results on a wide variety of natural language processing tasks. Whilst in the research stages, and prior to being added to production search systems, BERT achieved state-of-the-art results on 11 different natural language processing tasks. These natural language processing tasks include, amongst others, sentiment analysis, named entity determination, textual entailment (aka next sentence prediction), semantic role labeling, text classification and coreference resolution. BERT also helps with the disambiguation of words with multiple meanings known as polysemous words, in context.
Text is the most widely used means of communication today. This data is abundant but nevertheless complex to exploit within algorithms. For years, scientists have been trying to implement different techniques that enable computers to replicate some mechanisms of human reading. During the past five years, research disrupted the capacity of the algorithms to unleash the value of text data. It brings today, many opportunities for the insurance industry.Understanding those methods and, above all, knowing how to apply them is a major challenge and key to unleash the value of text data that have been stored for many years. Processing language with computer brings many new opportunities especially in the insurance sector where reports are central in the information used by insurers. SCOR's Data Analytics team has been working on the implementation of innovative tools or products that enable the use of the latest research on text analysis. Understanding text mining techniques in insurance enhances the monitoring of the underwritten risks and many processes that finally benefit policyholders.This article proposes to explain opportunities that Natural Language Processing (NLP) are providing to insurance. It details different methods used today in practice traces back the story of them. We also illustrate the implementation of certain methods using open source libraries and python codes that we have developed to facilitate the use of these techniques.After giving a general overview on the evolution of text mining during the past few years,we share about how to conduct a full study with text mining and share some examples to serve those models into insurance products or services. Finally, we explained in more details every step that composes a Natural Language Processing study to ensure the reader can have a deep understanding on the implementation.
We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) - term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology - SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). This transition will allow to generate more accurate semantic maps of different subject domains (also, of relations between input terms - it is useful to explore clusters and oppositions, or to test your hypotheses about them). The semantic map can be represented as a graph using Vec2graph - a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs. The Vec2graph library coupled with term embeddings will not only improve accuracy in solving standard NLP tasks, but also update the conventional concept of automated ontology development. The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.