Cognitive applications have become constant companions at our places of work. We expect smart systems to reduce repetitive workloads and support us in uncovering new Knowledge. As a result, data scientists and software engineers are applying various machine learning algorithms to finetune results and increase processing capabilities. At the same time, critics are ever more loudly calling for more transparency about how these cognitive applications actually function. Companies are also advised to not to manage their AI-driven application environment solely on technical grounds.
Recent scandals around fake news have spurred an interest in programmatically gauging the journalistic quality of an article. Companies like Factmata and Full Fact have received funding from Google, and Facebook has launched its "Journalism Project" earlier this year to fight the spread of fake stories in its feed. Discriminating between facts and fake information is a daunting task but often times, looking at the publisher is a good proxy to gauge the journalistic quality of an article. And while there is no objective metric to evaluate the quality of a newspaper, its overall quality and political bias is generally agreed upon (one can for example refer to https://mediabiasfactcheck.com/). In this article, we present a few techniques to automatically assess the journalistic quality of a newspaper.
In 2016 it got exponential growth over 2012 and 2017 figures till Sept equally critical is delivering this performance with reduced silicon (all grey & thin areas) area and industry power consumption. Machine learning helps in reducing the required efforts bandwidth between the buyer, seller and manufacturers such bandwidth reductions also reduce the cost and required time. In eCommerce AI based technologies like Big Data, Machine Learning, Neural Networks, Data Science, Bots and Deep Learning (mainly for secured online payments) are currently buzzwords. To safe guard the business from anti social elements deep learning helps in fraud detection, prevention, velocity measure and makes better business decisions with deep understanding of entity resolution (avoid multiple accounts of same person), Image recognition and understanding, Concept extraction, sentiment and trend analysis makes buyers life easy to choose and buy.
Let's define the semantic relatedness of two WordNet nouns x and y as follows: This is the notion of distance that we need to use to implement the distance() and sca() methods in the WordNet data type. As expected, potato is the outcast in the list of the nouns shown below (a noun with maximum distance from the rest of the nouns, all of which except potato are fruits, but potato is not). Again, as expected, table is the outcast in the list of the nouns shown below (a noun with maximum distance from the rest of the nouns, all of which except table are mammals, but table is not). Finally, as expected, bed is the outcast in the list of the nouns shown below (a noun with maximum distance from the rest of the nouns, all of which except bed are drinks, but bed is not).
These results confirm our observations that the fast cunning brown fox liked the slow canine dog is indeed the closest match to our query. Latent Semantic Analysis relies on a mathematical process called truncated Singular Value Decomposition (SVD) to reduce the dimensionality of the term document matrix. We can also see that our query "the cunning creature ran around the canine" strongly matches the document "The quick brown fox jumped over the lazy dog" even though they share no terms in common. We used tf-idf to weight the term frequencies according to how frequently the terms appeared across all the documents in the corpus thereby removing bias caused by commonly occuring words.
The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification. A popular and simple method of feature extraction with text data is called the bag-of-words model of text. An N-gram is an N-token sequence of words: a 2-gram (more commonly called a bigram) is a two-word sequence of words like "please turn", "turn your", or "your homework", and a 3-gram (more commonly called a trigram) is a three-word sequence of words like "please turn your", or "turn your homework". This addresses the problem of having a very large vocabulary for a large text corpus because we can choose the size of the hash space, which is in turn the size of the vector representation of the document.
Many people, especially the long-term practitioners in humanities and similar disciplines, find this change worrying, and in many ways exactly contrary to the spirit of these disciplines. However, the aim of this book is neither to teach R or programming, but to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. The book takes the freely available text file of "Moby Dick" and runs a variety of textual analysis on it: simple word count and word frequencies, correlations between various "special" words, context analysis, etc. Even though this is primarily a book intended for literature students, I would actually strongly recommend it to anyone interested in text mining, text analysis and natural language processing.
Have you ever considered delivering a project that utilizes Semantic Technology or a Graph Database to validate your business case? There is simply a lack of organized, consistent resources focused on practical knowledge. During the training, we take a look at the broad range of advantages Semantic Technology offers such as the integration of dynamic data from virtually unlimited sources, flexible data modeling, automated knowledge discovery, and data integration with Linked Open Data resources. We also focus on practical implementation scenarios via hands-on demonstration of building a small Proof-of-Concept project with high-level applications on top of a graph database.
By feeding the word vectorization algorithm a very large corpus (we are talking here about millions of words or more), we will obtain a vector mapping in which close values imply that the words appear in the same context and more generally have some kind of similarity, may it be syntactic or semantic. The tutorial below shows how to simply achieve and visualize a word vectorization using the Python Tensorflow library. However with these few words, the model was not good enough to perform meaningful operations between vectors. I guess Theodoers need to write more blog articles to feed the word vectorization algorithm!