The conventional resource search in cloud infrastructure relies on keyword-based searches or GUIDs, which demand exact matches and significant user effort to locate resources. These conventional search approaches often fail to interpret the intent behind natural language queries, making resource discovery inefficient and inaccessible to users. Though there exists some form of NLP based search engines, they are limited and focused more on analyzing the NLP query itself and extracting identifiers to find the resources. But they fail to search resources based on their behavior or operations or their capabilities or relationships or features or business relevance or the dynamic changing state or the knowledge these resources have. The search criteria has been changing with the inundation of AI based services which involved discovering not just the requested resources and identifiers but seeking insights. The real intent of a search has never been to just to list the resources but with some actual context such as to understand causes of some behavior in the system, compliance checks, capacity estimations, network constraints, or troubleshooting or business insights. This paper proposes an advanced Natural Language Processing (NLP) enhanced by ontology-based semantics to enable intuitive, human-readable queries which allows users to actually discover the intent-of-search itself. By constructing an ontology of cloud resources, their interactions, and behaviors, the proposed framework enables dynamic intent extraction and relevance ranking using Latent Semantic Indexing (LSI) and AI models. It introduces an automated pipeline which integrates ontology extraction by AI powered data crawlers, building a semantic knowledge base for context aware resource discovery.

Welcome to Machine Learning: Natural Language Processing in Python (Version 2). In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

Welcome to Machine Learning: Natural Language Processing in Python (Version 2). In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

Welcome to Machine Learning: Natural Language Processing in Python (Version 2). In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

The main focus of information retrieval methods is to provide accurate and efficient results which are cost-effective too. LINGO (Label Induction Grouping Algorithm) is a clustering algorithm that aims to provide search results in form of quality clusters but also has a few limitations. In this paper, our focus is based on achieving results that are more meaningful and improving the overall performance of the algorithm. LINGO works on two main steps; Cluster Label Induction by using Latent Semantic Indexing technique (LSI) and Cluster content discovery by using the Vector Space Model (VSM). As LINGO uses VSM in cluster content discovery, our task is to replace VSM with LSI for cluster content discovery and to analyze the feasibility of using LSI with Okapi BM25. The next task is to compare the results of a modified method with the LINGO original method. The research is applied to five different text-based data sets to get more reliable results for every method. Research results show that LINGO produces 40-50% better results when using LSI for content Discovery. From theoretical evidence using Okapi BM25 for scoring method in LSI (LSI+Okapi BM25) for cluster content discovery instead of VSM, also results in better clusters generation in terms of scalability and performance when compares to both VSM and LSI's Results.

The task of maintaining a WordPress site needs consistency and tenacity in order to have a profitable enterprise. This task might become a herculean one, time-consuming and an excruciating one if you are to work manually on all the WordPress plug-ins without any assistance. But with the advent of Artificial Intelligence (AI), most of the tasks on these WordPress plug-ins are thus simplified with the help of AI boosting the quality of your WordPress site and in turn, increasing your profitability. This is part I of the two-part articles. For the second part please check this link.

With 2017 nearing its end, one thing has become certain: search engine optimization and content marketing need to go hand-in-hand in order to survive and excel in the online ecosystem. Whether it was for Google's RankBrain algorithm filtering high-quality content or Latent Semantic Indexing for content relevance, the year brought SEO experts and content marketers together, who drafted successful marketing strategies as a team. Well, the same is expected to happen in 2018 but on a much grandiose scale. Although many lists for SEO and content marketing trends would do their rounds like every year, a new list of trends, focusing on the interdependence of content and SEO, would grasp the fancy of most marketers. It would encompass the trends in SEO and content that would help the marketers improve ranking and drive traffic on search engines in the coming year.

There are a ton of exciting new technologies on the way in the near future. These include the likes of virtual reality, augmented reality, artificial intelligence, IOT, personal assistants and more. We're taking tentative steps into the future and the next few years promise to be very exciting indeed for tech enthusiasts (that's you!). But when looking at these kinds of paradigm shifts, what's more important is the technology that lies beneath them. Keeping your ear to the floor and looking out for examples of new technology can therefore help you to better understand what might be around the corner.

I thought I'd write up an explanation of how Latent Semantic Indexing works since I used it in my most recent project PUBmatch.co PUBmatch.co is a tool I thought of while I was at Metis Data Science Bootcamp. You can see my slides from my presentation here. This post has a companion python notebook if you'd like to follow along here: Latent Semantic Indexing Notebook Latent Semantic Indexing appealed to me because it involves the use of Singular Value Decomposition. Singular Value Decomposition is at the heart of signal processing.

Cross-lingual text classification is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classification is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target language domain. In this work, we propose a novel semi-supervised representation learning approach to address this challenging task by inducing interlingual features via semi-supervised matrix completion. To evaluate the proposed learning technique, we conduct extensive experiments on eighteen cross language sentiment classification tasks with four different languages. The empirical results demonstrate the efficacy of the proposed approach, and show it outperforms a number of related cross-lingual learning methods.