Segmenting text into semantically coherent segments is an important task with applications in information retrieval and text summarization. Developing accurate topical segmentation requires the availability of training data with ground truth information at the segment level. However, generating such labeled datasets, especially for applications in which the meaning of the labels is user-defined, is expensive and time-consuming. In this paper, we develop an approach that instead of using segment-level ground truth information, it instead uses the set of labels that are associated with a document and are easier to obtain as the training data essentially corresponds to a multilabel dataset. Our method, which can be thought of as an instance of distant supervision, improves upon the previous approaches by exploiting the fact that consecutive sentences in a document tend to talk about the same topic, and hence, probably belong to the same class. Experiments on the text segmentation task on a variety of datasets show that the segmentation produced by our method beats the competing approaches on four out of five datasets and performs at par on the fifth dataset. On the multilabel text classification task, our method performs at par with the competing approaches, while requiring significantly less time to estimate than the competing approaches.

The quest of `can machines think' and `can machines do what human do' are quests that drive the development of artificial intelligence. Although recent artificial intelligence succeeds in many data intensive applications, it still lacks the ability of learning from limited exemplars and fast generalizing to new tasks. To tackle this problem, one has to turn to machine learning, which supports the scientific study of artificial intelligence. Particularly, a machine learning problem called Few-Shot Learning (FSL) targets at this case. It can rapidly generalize to new tasks of limited supervised experience by turning to prior knowledge, which mimics human's ability to acquire knowledge from few examples through generalization and analogy. It has been seen as a test-bed for real artificial intelligence, a way to reduce laborious data gathering and computationally costly training, and antidote for rare cases learning. With extensive works on FSL emerging, we give a comprehensive survey for it. We first give the formal definition for FSL. Then we point out the core issues of FSL, which turns the problem from "how to solve FSL" to "how to deal with the core issues". Accordingly, existing works from the birth of FSL to the most recent published ones are categorized in a unified taxonomy, with thorough discussion of the pros and cons for different categories. Finally, we envision possible future directions for FSL in terms of problem setup, techniques, applications and theory, hoping to provide insights to both beginners and experienced researchers.

The news industry is more dependent upon search-engine generated traffic than ever these days, which means when Saturday Night Live invites Game of Thrones star Kit Harington to host Saturday Night Live the week before Game of Thrones returns with the final season of Game of Thrones, then that same Kit Harington appears in a Saturday Night Live Game of Thrones sketch about upcoming Game of Thrones spinoffs that includes cameos from Law & Order: Special Victims Unit stars Mariska Hargitay and Ice T, Slate is going to make sure you Game of Thrones fans searching for Game of Thrones news find out about it, even if the sketch doesn't include plot details and spoilers for Game of Thrones' last season, a list of everyone who dies in the Game of Thrones finale, or confirmation that the Night King wins the Game of Thrones. We don't care if you use abbreviations like GoT or SNL or spell it Gaem of Throns: The important thing is that you typed something into a search bar and landed on this page of the internet. While you try to figure out why Google thought you'd find "Stairway to Heaven easy solo tablature" here, on a website that would not typically publish "Stairway to Heaven easy solo tablature," and indeed, has still not published "Stairway to Heaven easy solo tablature," perhaps you'd enjoy watching a Saturday Night Live sketch about Game of Thrones: Any sketch that lets Kyle Mooney deploy his 1990s sitcom delivery is a winner, but obviously Hodor's House is the spinoff to watch, because how could the second episode live up to the pilot? But there's a lot to love here for fans of Game of Thrones, Saturday Night Live, Pee-wee's Playhouse, Kit Harington, Ice T, Mariska Hargitay, HBO, Daria, Arya, the Game of Thrones finale, Beck Bennett, Heidi Gardner, the final season of Game of Thrones, Cecily Strong, Game of Thrones, Kyle Mooney, Game of Thrones final season spoilers, Pete Davidson, Game of Thrones, Game of Thrones, or even Game of Thrones. The final season of Game of Thrones begins on April 14.

Ranking high takes both time and effort. But there are some SEO techniques out there that are easy to implement and will definitely bring some results. You can probably do everything yourself. Spoiler alert, though: many of them include basics. During the years we have observed something rather shocking. Many clients that I've picked up had paid big time for search engine optimization services in the past. To my surprise, however, basic things like keywords in titles were missing. Now before you start, you can always perform an audit. It's not actually something easy to do, depending on the size of your website, but it will help you prioritize your actions in order to save time. Depending on each individual situation, different actions in the list below might not be useful or might not apply to your case.

Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In such a framework, vector quantization is used for coarse partitioning while product quantization is used for quantizing residuals. In the original IVFADC as well as all of its variants, after residuals are computed, the second production quantization step is completely independent of the first vector quantization step. In this work, we seek to exploit the connection between these two steps when we perform non-exhaustive search. More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively. The proposed method, called local orthogonal decomposition, combined with multiscale quantization consistently achieves higher recall than previous methods under the same bitrates. We conduct comprehensive experiments on large scale datasets as well as detailed ablation tests, demonstrating effectiveness of our method.

Information Retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events' possibly implicit, uncertain effects and side-effects. We begin by analyzing the problem, then propose an action language based formalization, and finally automate the corresponding IR task using Answer Set Programming.

Search engines understand very broad and generic information. This is why many of these search engine providers keep updating their search systems with different algorithms. For example Google search understands queries like movies, geography, images etc. This is not what a user wants. Users want to get more information and today's Google search engine cannot provide such information.

Even with the ongoing rise of social media, search engines remain key to product and business discovery - and as such, search engine optimization (SEO) remains an integral part of any effective digital marketing plan. But as with all elements of digital marketing, SEO is always changing, always evolving in line with user behaviors. Because of this, it's crucial that your SEO efforts also evolve in line in order to maximize your search opportunities. So what are the latest SEO trends that you need to know about? The team from Branex have put together this infographic of rising SEO shifts, and notes on how you can improve your strategy.

The 11th meeting of Forum for Information Retrieval Evaluation 2019 will be held in Kolkata, India. Started in 2008 with the aim of building a South Asian counterpart for TREC, CLEF and NTCIR, FIRE has since evolved continuously to meet the new challenges in multilingual information access. It has expanded to include new domains like plagiarism detection, legal information access, mixed script information retrieval and spoken document retrieval to name a few. Continuing the trend started in 2015, the FIRE will consist of a peer-reviewed conference track along with evaluation tasks. We invite full and short papers from information retrieval, natural language processing, and related domains.

As it and other technology giants face questions and fines over their practices when it comes to competition and user privacy, Google is adding a new official option to its popular Chrome browser that allows users to search the web using the privacy-focused DuckDuckGo search engine rather than its own platform. The update to Chromium -- which powers Google Chrome -- axes search engines like AOL and Yahoo!, replacing them with DuckDuckGo (in France, privacy-focused search engine Qwant was also added to the list). More search-savvy users may have already known about the company's DuckDuckGo Chrome extension, which makes DuckDuckGo the default option in the Google browser and protects users from ad-tracking software found on almost every site you visit regularly. The Chrome update means you will no longer need an extension to use DuckDuckGo from your URL bar. If you're unfamiliar, DuckDuckGo is a search engine designed to protect any data generated by your search results and history.