We invite you to experience the distinctive style of Alisa Hotels Accra conference rooms and facilities designed to accommodate small to large events with a state of the art array of technology and catering services to make your event a total success. Would you prefer to share this page with others by linking to it? The most excellent way to explain the definition of search engine,is to say, it is a website or an online service that collects and organizes content from all over the internet. If you wish to locate information on the internet,you would enter a query about what it is that you are searching for,the search engine provides links to content and information that matches the query you are searching for. Search engines use powerful computer software that has the capability of searching through huge volumes of text or other data for specified keywords and then returning a list of files or documents where the keywords were found ranked in order of relevance. Search engines make life easier for users by tracking down massive on-line information on a wide variety of topics and are valuable on-line sources of secondary data.

The world is getting data rich. Data is now a commodity for most businesses and a lot of this data is unstructured. This means that there is a need for Natural Language Processing (NLP). Natural language processing (NLP) is a branch of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. Its goal is to enable computers to communicate with humans in a natural way, i.e. by using language, rather than simple strings of symbols. NLP includes the tasks of speech recognition, speech synthesis, document retrieval, understanding natural language, machine translation, and information retrieval.

We generate content ideas based on your keyword search. Register now and get more DATA! You can use our service to find new topics, keywords, and phrases that you might not have thought of before. This will help improve your SEO and provide new content ideas for your blog or eCommerce product page. The best part is that it's free to register!

Why is my search engine Yahoo? It's a common question among webmasters, especially those who are just starting out in their internet endeavors. The reason is fairly simple: Yahoo is a very dominant search engine when it comes to searches. A lot of webmasters have grown dependent on the Search Marketing Working Group or SEM for backlinks and ranking. Therefore, if you want to make your page visible in Yahoo's result pages, it is important that you know how to increase your search engine positioning.

Level AI is a Mountain View, CA-based startup innovating in the Voice AI space. We are backed by top VCs, technologists from Silicon Valley and industry experts. We are on a mission to revolutionize the customer sales experience for businesses. We are innovating in speech AI, NLP and information retrieval systems to bring customers and businesses closer to one another. As one of the critical members of the Level team your work will be new and of the highest impact to shape the future of AI in businesses.

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models are based on the probabilistic graphical model (PGM) framework, which requires manually designed dependencies and may oversimplify user behaviors. Recently, methods based on neural networks are proposed to improve the prediction accuracy of user behaviors by enhancing the expressive ability and allowing flexible dependencies. However, they still suffer from the data sparsity and cold-start problems. In this paper, we propose a novel graph-enhanced click model (GraphCM) for web search. Firstly, we regard each query or document as a vertex, and propose novel homogeneous graph construction methods for queries and documents respectively, to fully exploit both intra-session and inter-session information for the sparsity and cold-start problems. Secondly, following the examination hypothesis, we separately model the attractiveness estimator and examination predictor to output the attractiveness scores and examination probabilities, where graph neural networks and neighbor interaction techniques are applied to extract the auxiliary information encoded in the pre-constructed homogeneous graphs. Finally, we apply combination functions to integrate examination probabilities and attractiveness scores into click predictions. Extensive experiments conducted on three real-world session datasets show that GraphCM not only outperforms the state-of-art models, but also achieves superior performance in addressing the data sparsity and cold-start problems.

Welcome to the World's best specialized SEO course ever. This is the only course in the world where you woll also learn about the technicalities of SEO and how to handle them. The content of this course is based on real world practices and checklists used by professionals in the SEO world. The content of the course focuses on giving the idea of how any SEO agency or freelancer approaches to any website and start the SEO to rank any particular keyword. You will understand how the SEO activities affect the website in terms of visibility by Search Engine.

Over the past decades, researchers had put lots of effort investigating ranking techniques used to rank query results retrieved during information retrieval, or to rank the recommended products in recommender systems. In this project, we aim to investigate searching, ranking, as well as recommendation techniques to help to realize a university academia searching platform. Unlike the usual information retrieval scenarios where lots of ground truth ranking data is present, in our case, we have only limited ground truth knowledge regarding the academia ranking. For instance, given some search queries, we only know a few researchers who are highly relevant and thus should be ranked at the top, and for some other search queries, we have no knowledge about which researcher should be ranked at the top at all. The limited amount of ground truth data makes some of the conventional ranking techniques and evaluation metrics become infeasible, and this is a huge challenge we faced during this project. This project enhances the user's academia searching experience to a large extent, it helps to achieve an academic searching platform which includes researchers, publications and fields of study information, which will be beneficial not only to the university faculties but also to students' research experiences.

We rank all we rely on item retrieval from marketplace inventory. With retrieved items by the sum of tf-idf scores from matched words, feedback to expand query scope, we discuss keyword expansion and keep the items with total tf-idf scores above a threshold. The candidate selection using word embedding similarity, and an retrieval based system works well to discover relevant enhanced tf-idf formula for expanded words in search ranking.

This research explores temporal concept drift and temporal alignment in knowledge organization systems (KOS). A comparative analysis is pursued using the 1910 Library of Congress Subject Headings, 2020 FAST Topical, and automatic indexing. The use case involves a sample of 90 nineteenth-century Encyclopedia Britannica entries. The entries were indexed using two approaches: 1) full-text indexing; 2) Named Entity Recognition was performed upon the entries with Stanza, Stanford's NLP toolkit, and entities were automatically indexed with the Helping Interdisciplinary Vocabulary application (HIVE), using both 1910 LCSH and FAST Topical. The analysis focused on three goals: 1) identifying results that were exclusive to the 1910 LCSH output; 2) identifying terms in the exclusive set that have been deprecated from the contemporary LCSH, demonstrating temporal concept drift; and 3) exploring the historical significance of these deprecated terms. Results confirm that historical vocabularies can be used to generate anachronistic subject headings representing conceptual drift across time in KOS and historical resources. A methodological contribution is made demonstrating how to study changes in KOS over time and improve the contextualization of historical humanities resources.