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We are excited to announce that we are launching a new section today at Sciforce — “Top AI news of the month.” We decided to create this column to keep our valued clients aware of the latest news and technologies in the AI world. So, here, we will share some interesting information with you about SciTech! Well, today we will discuss Top-5 NLP news in December:

As soon as you start working on a data science task you realize the dependence of your results on the data quality. The initial step — data preparation — of any data science project sets the basis for the effective performance of any sophisticated algorithm. In textual data science tasks, this means that any raw text needs to be carefully preprocessed before the algorithm can digest it. In the most general terms, we take some predetermined body of text and perform upon it some basic analysis and

The NLP domain reports great advances to the extent that a number of problems, such as part-of-speech tagging, are considered to be fully solved. At the same time, such tasks as text summarization or machine dialog systems are notoriously hard to crack and remain open for the past decades. However, if we look deeper into such tasks we’ll see that the problems behind them are rather similar and fall into two groups:

We write a lot about open problems in Natural Language Processing. We complain a lot when working on NLP projects. We pick on inaccuracies and blatant errors of different models. But what we need to admit is that NLP has already changed and new models have solved the problems that may still linger in our memory. One of such drastic developments is the launch of Google’s Bidirectional Encoder Representations from Transformers, or BERT model — the model that is called the best NLP model ever based

As artificial intelligence progresses and technology becomes more sophisticated, we expect existing concepts to embrace this change — or change themselves. Similarly, in the domain of computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated than merely the linear progress of a technology? In this post, we’ll scrutinize the concepts of NLP an

Integration and interdisciplinarity are the cornerstones of modern science and industry. One of the examples of recent attempts to combine everything is the integration of computer vision and natural language processing (NLP). Both these fields are one of the most actively developing machine learning research areas. Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. It is now, with the expansion of multimedia, researchers have started expl

No matter what technology domain you are looking at, the unanimous expectation of the next year is that natural language processing will take the leading role in this domain advancement. Be it Business Intelligence, FinTech, or Healthcare, NLP seems to become a field-shaping technology. In 2018, businesses used NLP techniques in several areas:

When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities: should you choose a classic for a solid base or a fresh-from-the-oven book for the newest trends? What level to stick to? Will a beginner’s guide be too easy? In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts. by Steven Bird, Ewan Klein and Edward Loper. It is so popular, that every top seems to have it listed. Wel