Search systems on the Web rely on user input to generate relevant results. Since early information retrieval systems, users are trained to issue keyword searches and adapt to the language of the system. Recent research has shown that users often withhold detailed information about their initial information need, although they are able to express it in natural language. We therefore conduct a user study (N = 139) to investigate how four different design variants of search interfaces can encourage the user to reveal more information. Our results show that a chatbot-inspired search interface can increase the number of mentioned product attributes by 84% and promote natural language formulations by 139% in comparison to a standard search bar interface.

Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conversational search is an essential ability to understand what the user really wants. However, evaluation datasets with natural and detailed information needs of product-seekers which could be used for research do not exist. Due to privacy issues and competitive consequences, only few datasets with real user search queries from logs are openly available. In this paper, we present a dataset of 3,540 natural language queries in two domains that describe what users want when searching for a laptop or a jacket of their choice. The dataset contains annotations of vague terms and key facts of 1,754 laptop queries. This dataset opens up a range of research opportunities in the fields of natural language processing and (interactive) information retrieval for product search.

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents.

The journalism industry has undergone a revolution in the past decade, leading to new opportunities as well as challenges. News consumption, production and delivery have all been affected and transformed by technology Readers require new mechanisms to cope with the vast volume of information in order to be informed about news events. Reporters have begun to use natural language processing (NLP) and (IR) techniques for investigative work. Publishers and aggregators are seeking new business models, and new ways to reach and retain their audience. A shift in business models has led to a gradual shift in styles of journalism in attempts to increase page views; and, far more concerning, to real mis- and dis-information, alongside allegations of "fake news" threatening the journalistic freedom and integrity of legitimate news outlets. Social media platforms drive viewership, creating filter bubbles and an increasingly polarized readership. News documents have always been a part of research on information access and retrieval methods. Over the last few years, the IR community has increasingly recognized these challenges in journalism and opened a conversation about how we might begin to address them. Evidence of this recognition is the participation in the two previous editions of our NewsIR workshop, held in ECIR 2016 and 2018. One of the most important outcomes of those workshops is an increasing awareness in the community about the changing nature of journalism and the IR challenges it entails. To move yet another step forward, the goal of the third edition of our workshop will be to create a multidisciplinary venue that brings together news experts from both technology and journalism. This would take NewsIR from a European forum targeting mainly IR researchers, into a more inclusive and influential international forum. We hope that this new format will foster further understanding for both news professionals and IR researchers, as well as producing better outcomes for news consumers. We will address the possibilities and challenges that technology offers to the journalists, the challenges that new developments in journalism create for IR researchers, and the complexity of information access tasks for news readers.

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.

We present a supervised approach to automatically labelling topic clusters of reader comments to online news.We use a feature set that includes both features capturing properties local to the cluster and features that capture aspects from the news article and from comments outside the cluster.We evaluate the approach in an automatic and a manual, task-based setting.Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels.We illustrate how cluster labels can be used to generate cluster summaries and present two alternative summary formats: a pie chart summary and an abstractive summary.

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news.To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult.In this paper we propose one type of reader comment summary -the conversation overview summary -that aims to capture the key argumentative content of a reader comment conversation.We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpusthe first of its kind -of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.

Online commenting to news articles provides a communication channel between media professionals and readers offering a crucial tool for opinion exchange and freedom of expression.Currently, comments are detached from the news article and thus removed from the context that they were written for.In this work, we propose a method to connect readers' comments to the news article segments they refer to.We use similarity features to link comments to relevant article segments and evaluate both word-based and term-based vector spaces.Our results are comparable to state-of-theart topic modeling techniques when used for linking tasks.We demonstrate that article segments and comments representation are relevant to linking accuracy since we achieve better performances when similarity features are computed using similarity between terms rather than words.

In this article, we investigate the application of entity type models in extractive multi‐document summarization using automatic caption generation for images of geo‐located entities (e.g., W estminster A bbey ) as an application scenario. Entity type models contain sets of patterns aiming to capture the ways geo‐located entities are described in natural language. They are automatically derived from texts about geo‐located entities of the same type (e.g., churches, lakes). We integrate entity type models into a multi‐document summarizer and use them to address the 2 major tasks in extractive multi‐document summarization: sentence scoring and summary composition . We experiment with 3 different representation methods for entity type models: signature words , n‐gram language models, and dependency patterns . We evaluate the summarizer with integrated entity type models relative to (a) a summarizer using standard text‐related features commonly used in text summarization and (b) the W ikipedia location descriptions. Our results show that entity type models significantly improve the quality of output summaries over that of summaries generated using standard summarization features and W ikipedia summaries. The representation of entity type models using dependency patterns is superior to the representations using signature words and n‐gram language models.

In this article, we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and Bodiam Castle are two objects of the same type: “castle.” The question is whether specific information is requested for the object type “castle” and how this information differs for objects of other types (e.g., church, museum, or lake). We aim to answer this question using an online survey. In the survey, we showed 184 participants 200 images pertaining to urban and rural objects and asked them to write questions for which they would like to know the answers when seeing those objects. Our analysis of the 6,169 questions collected in the survey shows that humans have shared ideas of what to ask about geographical objects. When the object types resemble each other (e.g., church and temple), the requested information is similar for the objects of these types. Otherwise, the information is specific to an object type. Our results may be very useful in guiding Natural Language Processing tasks involving automatic generation of templates for image descriptions and their assessment, as well as image indexing and organization.

In this paper we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and the Bodiam Castle are two objects of the same type - castle. The question is whether specific information is requested for the object type castle and how this information differs for objects of other types, e.g. church, museum or lake. We aim to answer this question using an online survey. In the survey we showed 184 participants 200 images pertaining to urban and rural objects and asked them to write questions for which they would like to know the answers when seeing those objects. Our analysis of 7644 questions collected in the survey shows that humans have shared ideas of what to ask about geographical objects. When the object types resemble each other (e.g. church, temple) the requested information is similar for the objects of these types. Otherwise, the information is specific to an object type. Our results can guide tasks involving automatic generation of templates for image descriptions, and their assessment as well as image indexing and organization.