Amalgam

Measuring semantic similarity between documents has varied applications in NLP and  Artificial sentences such as in chatbots, voicebots, communication in different languages etc. . It refers to quantifying similarity of sentences based on their literal meaning rather than only syntactic structure.  A semantic net such as WordNet and Word Embeddings such as Google’s Word2Vec, DocToVec can be used to compute semantic similarity. Let us see how. Word Embeddings Word embeddings are vector representations of words. A word embedding tries to map a word to a numerical vector representation using a dictionary of words, i.e. words and phrases from the vocabulary are mapped to the vector space and represented using real numbers. The closeness of vector representations of 2 words in the real space is a measure of similarity between them. Word embeddings can be broadly classified into frequency based (eg: count vector, tfidf, co occurrence etc) and prediction based (eg: Continuous bag

INTRODUCTION Natural Language processing has been used to solve many problems such as machine translation, sentiment analysis, parts of speech tagging and many more. On the other hand Image analysis is also shifting its gear up in the era of upcoming technology. Facebook’s face tagging, object recognition are some of the applications that come under image analysis. Both domains are a part of Artificial Intelligence but are much independent of each other. Now what if we could combine the powers of these two to solve many more interesting problems. Let’s discuss some real life problems that can be solved by these two techniques for better understanding of their working extents: APPLICATIONS One of the most significant recent advances in health information systems has been the shift from paper to electronic documents. Now as we are aware that medical stuff involves much image components in form of x-rays scans, MRI scans, etc. accompanied by a short text descripti

Introduction There’s a lot of buzzword around the term “Sentiment Analysis” and the various ways of doing it. Great! So you report with reasonable accuracy what the sentiment about a particular brand or product is. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy. Over time, some powerful methods have been developed using Natural Language Processing and computational linguistics to extract these subjective opinions. An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand th

How many of you get frustrated while shopping ?Looking for something but not able to find it. How about Finding the right item via "Conversation" ?Not with humans but a service that stays with you all throughout ?  Is this what you are looking for ? If your answer is yes ,then welcome to the world of Chatbots . Chatbots are software programs , that are meant for interaction between a human and machine in the best possible natural manner i.e. with the use of natural language . So, basically a bot with intelligent quotient as good as a human qualifies to be called as  good Chatbot , so that it simulates the responses that would generally be given while interacting with a human .  For a Chatbot to qualify as a intelligent chatbot it has to pass through   "Turing test". Turing test involves the conversation with judges and this conversation goes as long as 5 minutes .All those who generate 30% correct answers pass the test . Combination of Machine Learnin

       Financial Institutions already use the well-mined data from well structured data-sources to enhance their day-to-day operations as well as actionable insights thus improving their business practices. Nowadays, a major part of the financial industry is turning towards the extraction of unstructured data with the help of Natural Language Processing, Artificial Intelligence, Information Retrieval and Machine Learning. These financial institutions have realised the existence of an enormous amount of unstructured data that can be gathered from social media, internet, personal devices, emails, text messages, images, videos, audios, news, articles, feedbacks, etc, which can be analysed and used to present these enterprises with unprecedented opportunities of growth. With the help of such unstructured data, companies get a better insight of the likability of their services and products amongst their customers and their competitors. Recent advances in technology and th