Abstract
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as "Philip Seymour Hoffman" where the results may require no recency at all. In this work, we focus on this type of queries named "time-sensitive queries" where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.

Abstract
Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity. For each query, we develop features from its search volumes and related queries using Google trends and its related top Wikipedia pages. Our experiment results show that these features can determine temporal ambiguity of a given query with high accuracy. We have demonstrated that a Multilayer Perceptron Networks can achieve better results in classifying temporal class of queries in comparison to other classifiers.

Abstract
The news industry has gone through seismic shifts in the past decade with digital content and social media completely redefining how people consume news. Readers check for accurate fresh news from multiple sources throughout the day using dedicated apps or social media on their smartphones and tablets. At the same time, news publishers rely more and more on social networks and citizen journalism as a frontline to breaking news. In this new era of fast-flowing instant news delivery and consumption, publishers and aggregators have to overcome a great number of challenges. These include the verification or assessment of a source’s reliability; the integration of news with other sources of information; real-time processing of both news content and social streams in multiple languages, in different formats and in high volumes; deduplication; entity detection and disambiguation; automatic summarization; and news recommendation. Although Information Retrieval (IR) applied to news has been a popular research area for decades, fresh approaches are needed due to the changing type and volume of media content available and the way people consume this content. The goal of this workshop is to stimulate discussion around new and powerful uses of IR applied to news sources and the intersection of multiple IR tasks to solve real user problems. To promote research efforts in this area, we released a new dataset consisting of one million news articles to the research community and introduced a data challenge track as part of the workshop. © Springer International Publishing Switzerland 2016.

Abstract
Recently, many historical texts have become digitized and made accessible for search and browsing. Professionals who work with collections of such texts often need to verify the correctness of documents' key metadata-their creation dates. In this paper, we demonstrate an interactive system for estimating the age of documents. It may be useful not only for tagging a large number of undated documents, but also for verifying already known timestamps. In order to infer probable dates, we rely on a large scale lexical corpora, Google Books Ngrams. Besides estimating the document creation year, the system also outputs evidences to support age detection and reasoning process and allows testing different hypotheses about document's age.

Abstract
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document. To understand the merits of our proposal, we compare it against RAKE, TextRank and SingleRank methods (three well-known unsupervised approaches) and the baseline TF. IDF, over four different collections to illustrate the generality of our approach. The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.

Abstract
In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages. Unlike most systems, YAKE! does not rely on dictionaries or thesauri, neither it is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in many different languages without the need for external knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. In this demo, we offer an easy to use, interactive session, where users from both academia and industry can try our system, either by using a sample document or by introducing their own text. As an add-on, we compare our extracted keywords against the output produced by the IBM Natural Language Understanding (IBM NLU) and Rake system. YAKE! demo is available at http://bit.ly/YakeDemoECIR2018. A python implementation of YAKE! is also available at PyPi repository (https://pypi.python.org/pypi/yake/).