Semantic Similarity, or Semantic Textual Similarity, is a task in the area of Natural Language Processing (NLP) that scores the relationship between texts or documents using a defined metric. Semantic Similarity has various applications, such as information retrieval, text summarization, sentiment analysis, etc. There have been a lot of approaches for Semantic Similarity. The most straightforward and effective method now is to use a powerful model (e.g. The similarity score indicates whether two texts have similar or more different meanings.
In this article I want to share about the evolution of text analysis algorithms in last decade. Natural Language(NLP)has been around for a long time, In fact, a very simple bag of words model was introduced in the 1950s. But in this article I want to focus on evolution of NLP during recent times. There has been enormous progress in the field since 2013 due to the evolution and the advancement of machine learning algorithms together with reduced cost of computation and memory. In 2013, a research team led by Thomas Michael off at Google introduced the Word2Vec algorithm.
Regular Expressions, also known as "regex" or "regexp", are used to match strings of text such as particular characters, words, or patterns of characters. It means that we can match and extract any string pattern from the text with the help of regular expressions. I have used two terms, match and extract and both the terms have a slightly different meaning. There may be cases when we want to match a specific pattern but extract a subset of it. For example, we want to extract the names of PhD scholars from a list of names of people in an organization. In this case, we will match the "Dr XYZ" keyword and extract only the name, i.e. "XYZ" not the prefix "Dr." from the list.
The avalanche of new data generated by these gadgets will enable carriers to understand their customers better, leading to new product categories, more tailored pricing, and increasingly real-time service delivery. FREMONT, CA: The disruption caused by COVID-19 shifted the timetables for AI adoption by considerably speeding up insurers' digitalization. The underlying AI technologies are already in use in the workplaces, homes, vehicles, and bodies. Organizations must react almost immediately to accommodate remote workers, extend their digital capabilities to facilitate distribution, and modernize their web channels. While most firms did not engage extensively in AI during the epidemic, the increased emphasis on digital technology and a more substantial openness to embracing change will enable them to integrate AI into their operations.
You might ask yourself questions such as what is the fastest path to a career in AI, or what is the best programming language for AI? The answer to these questions will depend on your knowledge and experience, the type of AI project you are interested in, and current industry trends. There is currently no dedicated AI language dedicated to this area of technology, but it does support many popular programming languages. However, in order to increase your chances of quickly launching a career in AI, you need to learn AI programming languages that are supported by several machine learning (ML) and deep learning libraries. For AI programming languages, Python is leading the way with its unparalleled community support and pre-built libraries that help accelerate AI development.
Natural Language Processing is the fastest-growing subset of AI that applies linguistics and computer science to make human language understandable to machines. There are new advancements every year. New tools of NLP are evolving and the old ones are being updated with more developed features. Before going with the top 10 NLP tools services, it is important to mention that all the tools are either recently released or are upgraded with new features. The tools named below are free and open-source instruments. Natural Language Toolkit, one of the leading tools for NLP, renders a whole set of programs and libraries to execute statistical and symbolic analysis in Python.
Human beings are the most advanced species on earth. There's no doubt in that and our success as human beings is because of our ability to communicate and share information but that's where the concept of developing a language comes in. We talk about the human language it is one of the most diverse and complex part of us considering a total of 6500 languages that exist century according to industry estimates only 21% of the available data is present in the structured form. Data is being generated at least be treat in send messages on WhatsApp or areas of the groups of Facebook and majority of this data accessed in the textual form, which is highly unstructured in nature. Now in order to produce significant actionable insights from this data it is important to get the techniques of text analysis and natural language processing so let's understand.
Named Entity Recognition (NER) is also known as "Entity Identification". It is a Natural Language Processing (NLP) technique that seeks to locate and classify named entities mentioned in any form of unstructured text. Each word is identified in predefined categories like Organization, Place, Person, Time Expressions, Quantities, Monetary Values, Percentages, etc. Extraction of named entities from unstructured contextual data is beneficial for analyzing different types of textual data. With tremendous advancements in NLP, machines are getting smarter. They can now intelligently understand large volumes of textual data that result in numerous use-cases like machine translation, text summarization, etc. Named Entity Recognition is a sub-task of information extraction.
Data analysis is one of the primary steps before machine learning models can be trained. This is because analysis of the data helps us in finding hidden patterns which can then be used to train machine learning models efficiently. We as humans need to first understand and be comfortable with the data even before the machine begins to learn on it, else it may lead to GIGO (Garbage-In, Garbage-Out). Below are some considerations to make before training an entity extraction model to extract business entities. Understanding business context in which different entities are used in a document is important.