The World Wide Web (WWW) abounds with ever-increasing information on many topics. However, since every user has specific information needs and interests, only a tiny part of the WWW is useful to them. For example, in a family, a mother may wish to "find recipes with salmon as the main ingredient", the father may be interested in "what movie to watch tonight?", and the teenage daughter may be wondering "what is artificial intelligence?". In order for humans to quickly ‘retrieve’ relevant information of interest, they usually search the Web using a search engine such as Google.
Although it sounds simple, information retrieval is a complex field involving many sub-tasks and applications. According to "the father of information retrieval", Gerard Salton, information retrieval is the field concerned with the tasks of structure, analysis, organization, storage, searching, and retrieval of information. Applications include, but are not limited to, web search (i.e., searching the WWW) which is the most common type, where the search is specialized in a specific topic only (e.g., searching for shoes within the football topic implies someone looking for football shoes), enterprise search, which involves searching for documents in a corporate intranet, image search, which is searching for images similar to a given image, product search, which involves searching for products similar to a given product, desktop search, which is searching for relevant files in our personal computer, or mobile search, which typically takes location and time into account. Users can be searching for different kinds of items, such as webpages, emails, scholarly papers, books, news stories, or even social profiles. Furthermore, with the advent of new technologies and modalities like virtual reality, it is likely that the scope of information retrieval will only increase with time.
Regardless of the type of search and the type of the returned item, the goal of every information retrieval algorithm is to take a search query as input, and to quickly find and output a ranked list of relevant items, i.e., items that contain information that the user was looking for. For example, in our family example, the mother may submit a query of the form "find recipes with salmon" and the expected result is an ordered (ranked) list of recipes containing salmon, ordered by how relevant each recipe is to the query. Although a straightforward approach would be for a retrieval algorithm to simply compare the query text with the recipe text, this approach will not always work due to language ambiguity. For example, when someone submits a query containing the single word "jaguar" it is very difficult for any algorithm to determine whether the user is looking for documents about jaguar the animal or jaguar the vehicle brand. To be effective, an information retrieval system needs to pay special attention to the meaning of queries rather than the actual words used in them.
Along with ambiguity, information retrieval faces a number of important challenges e.g., dealing with unstructured information, ensuring that it takes each user's context and expectations into account when returning the results, and dealing with scalability (e.g., search engines typically index and search almost instantly, billions of items, in order to answer each user's query, along with answering more than a trillion queries per year). Researchers are continuing to address these challenges.
- Pigi Kouki
Search is an inextricable and important aspect which is even more relevant in business content management systems when it comes to the making and adaption of digital content. The relevance of search is nothing new especially considering that getting a suitable application for any business procedures. Interestingly, the need for search continues to heighten particularly with the growing application of big data. It is safe to conclude that business data holds the riches of a business and search is the instrument that unleashes the riches. However, the question remains that what is an effective way to handle the continuously big data that businesses possess?
With over 500 million active monthly users, Instagram is a gold mine of potential customers. Can you believe the app has only been around since 2010? In less than seven years, it's become one of the world's most used apps. I daresay Instagram even changed the way people use their phone in their daily lives. The biggest reason to use Instagram for business?
If you do digital marketing for a small business, Facebook can be a great way to drive business to your store. Hopefully, you have a website to virtually display your business, but social media can be a great addition to showing people what you sell or what solution you solve for. Facebook should definitely be one area you focus on. It doesn't take a huge investment on your part. Here are five ways to promote your local business on Facebook.
Data Science uses scientific methods algorithms and processes to extract insights from data in various forms. Artificial Intelligence has adopted a pattern of learning that assesses the response of its users. When use search engines and the first page doesn't deliver the required information, the user goes to another page immediately. Interestingly, AI monitors this response to understand your behavior. When you eventually discover the page you require and remain on the page for some time, you can be sure that AI monitors your entire behavior.
Search engines exist to provide users with results that are relevant to the search query. Smart SEO campaigns are built on an understanding of how your audience searches around your industry, products and services. A key point here is understanding the intent behind a given keyword search. A user wants to find specific information, and search engines have advanced algorithms and large amounts of traffic they analyze to determine which results are the best match for a keyword. Understanding the broad categories of intent is crucial to developing a search engine optimization and content strategy to target not only the keywords but the intent behind the keywords.
Google's secretive plans in China are attracting renewed scrutiny from privacy advocates. The tech giant is said to be building a prototype version of a censored Chinese search engine that links users' activity to their personal phone number, according to the Intercept. In doing so, it would be able to comply with the Chinese government's censorship requirements, increasing the chances that such a product would launch there in the future. A bipartisan group of 16 US lawmakers asked Google if it would comply with China's internet censorship and surveillance policies should it re-enter the search engine market there While China is home to the world's largest number of internet users, a 2015 report by US think tank Freedom House found that the country had the most restrictive online use policies of 65 nations it studied, ranking below Iran and Syria. But China has maintained that its various forms of web censorship are necessary for protecting its national security.
Imagine you're on the Tube and the person in front of you is wearing a really nice pair of trainers. To find them, you could search for "black suede trainers with off-white soles" and leaf through hundreds of possible results. Or, in a world of perfectly accurate visual search, you could find and buy the exact pair instantly from a picture. Three-quarters (74%) of consumers agree that text based keyword searches are inefficient in helping to find the right product online. This opportunity gap will be explored at Dmexco this week in a number of sessions dedicated to smarter search, and it emphasises that brands need to prepare themselves for visual search.
With an ever growing number of extractive summarization techniques being proposed, there is less clarity then ever about how good each system is compared to the rest. Several studies highlight the variance in performance of these systems with change in datasets or even across documents within the same corpus. An effective way to counter this variance and to make the systems more robust could be to use inputs from multiple systems when generating a summary. In the present work, we define a novel way of creating such ensemble by exploiting similarity between the content of candidate summaries to estimate their reliability. We define GlobalRank which captures the performance of a candidate system on an overall corpus and LocalRank which estimates its performance on a given document cluster. We then use these two scores to assign a weight to each individual systems, which is then used to generate the new aggregate ranking. Experiments on DUC2003 and DUC 2004 datasets show a significant improvement in terms of ROUGE score, over existing sate-of-art techniques.
In this paper, we describe DeFactoNLP, the system we designed for the FEVER 2018 Shared Task. The aim of this task was to conceive a system that can not only automatically assess the veracity of a claim but also retrieve evidence supporting this assessment from Wikipedia. In our approach, the Wikipedia documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are most similar to the vector of the claim and those documents whose names are similar to those of the named entities (NEs) mentioned in the claim are identified as the documents which might contain evidence. The sentences in these documents are then supplied to a textual entailment recognition module. This module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information to assess the veracity of the claim. Various features computed using these probabilities are finally used by a Random Forest classifier to determine the overall truthfulness of the claim. The sentences which support this classification are returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a 0.5136 label accuracy and a 0.3833 FEVER score.
Search has always been a key enterprise technology going back to the days of the first enterprise content management systems. This is hardly surprising given how important finding the right data is for any of the applications used by enterprises in their business processes. Since the rise of big data and the use of big data sets, search has become even more important. If enterprise data is the real wealth of a business, then search is the tool that uncovers that wealth. But what do you do with the increasingly large amounts of data that enterprises now have access to?