Mnews: A Study of Multilingual News Search Interfaces

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, particularly in terms of languages. In fact, the number of polyglot Web users across the globe has increased dramatically. However, even such multilingual users often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages, even though prior research results have shown that multilingual users make significant use of each of their languages when searching for information online. Relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences. In particular, in the fields of cross-language and multilingual search, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. The research presented in this thesis provides the first large-scale investigations of multilingual news consumption and querying/search result selection behaviors, as well as a detailed comparative analysis of polyglots’ preferences and behaviors with respect to different multilingual news search interfaces on desktop and mobile platforms. Through a set of 4 phases of user studies, including surveys, interviews, as well as task-based user studies using crowdsourcing and laboratory experiments, this thesis presents the first human-centered studies in multilingual news access, aiming to drive the development of personalized multilingual news access systems to better support each individual user

Exploring Topic-Language Preferences in Multilingual Swahili Information Retrieval in Tanzania

Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences - topic-language preferences. This paper is based on a carefully controlled study using Swahili speaking Web users in Tanzania who interacted with a guided multilingual search engine. From the statistical analysis of queries and click-through logs, it was revealed that language preferences may be associated with the topics of search. The results also suggest that language preferences are not static; they vary along the course of Web search from query to results selection. In most of the topics, users either had significantly no language preference or preferred to query in Kiswahili and changed their preference to either English or no preference for language when selecting/clicking on the results. The findings of this study might provide researchers with more insights in developing better MLIR systems that support certain types of users and in certain scenarios

Large-scale Affective Computing for Visual Multimedia

In recent years, Affective Computing has arisen as a prolific interdisciplinary field for engineering systems that integrate human affections. While human-computer relationships have long revolved around cognitive interactions, it is becoming increasingly important to account for human affect, or feelings or emotions, to avert user experience frustration, provide disability services, predict virality of social media content, etc. In this thesis, we specifically focus on Affective Computing as it applies to large-scale visual multimedia, and in particular, still images, animated image sequences and video streams, above and beyond the traditional approaches of face expression and gesture recognition. By taking a principled psychology-grounded approach, we seek to paint a more holistic and colorful view of computational affect in the context of visual multimedia. For example, should emotions like 'surprise' and `fear' be assumed to be orthogonal output dimensions? Or does a 'positive' image in one culture's view elicit the same feelings of positivity in another culture? We study affect frameworks and ontologies to define, organize and develop machine learning models with such questions in mind to automatically detect affective visual concepts. In the push for what we call "Big Affective Computing," we focus on two dimensions of scale for affect -- scaling up and scaling out -- which we propose are both imperative if we are to scale the Affective Computing problem successfully. Intuitively, simply increasing the number of data points corresponds to "scaling up". However, less intuitive, is when problems like Affective Computing "scale out," or diversify. We show that this latter dimension of introducing data variety, alongside the former of introducing data volume, can yield particular insights since human affections naturally depart from traditional Machine Learning and Computer Vision problems where there is an objectively truthful target. While no one might debate a picture of a 'dog' should be tagged as a 'dog,' but not all may agree that it looks 'ugly'. We present extensive discussions on why scaling out is critical and how it can be accomplished while in the context of large-volume visual data. At a high-level, the main contributions of this thesis include: Multiplicity of Affect Oracles: Prior to the work in this thesis, little consideration has been paid to the affective label generating mechanism when learning functional mappings between inputs and labels. Throughout this thesis but first in Chapter 2, starting in Section 2.1.2, we make a case for a conceptual partitioning of the affect oracle governing the label generation process in Affective Computing problems resulting a multiplicity of oracles, whereas prior works assumed there was a single universal oracle. In Chapter 3, the differences between intended versus expressed versus induced versus perceived emotion are discussed, where we argue that perceived emotion is particularly well-suited for scaling up because it reduces the label variance due to its more objective nature compared to other affect states. And in Chapter 4 and 5, a division of the affect oracle along cultural lines with manifestations along both language and geography is explored. We accomplish all this without sacrificing the 'scale up' dimension, and tackle significantly larger volume problems than prior comparable visual affective computing research. Content-driven Visual Affect Detection: Traditionally, in most Affective Computing work, prediction tasks use psycho-physiological signals from subjects viewing the stimuli of interest, e.g., a video advertisement, as the system inputs. In essence, this means that the machine learns to label a proxy signal rather than the stimuli itself. In this thesis, with the rise of strong Computer Vision and Multimedia techniques, we focus on the learning to label the stimuli directly without a human subject provided biometric proxy signal (except in the unique circumstances of Chapter 7). This shift toward learning from the stimuli directly is important because it allows us to scale up with much greater ease given that biometric measurement acquisition is both low-throughput and somewhat invasive while stimuli are often readily available. In addition, moving toward learning directly from the stimuli will allow researchers to precisely determine which low-level features in the stimuli are actually coupled with affect states, e.g., which set of frames caused viewer discomfort rather a broad sense that a video was discomforting. In Part I of this thesis, we illustrate an emotion prediction task with a psychology-grounded affect representation. In particular, in Chapter 3, we develop a prediction task over semantic emotional classes, e.g., 'sad,' 'happy' and 'angry,' using animated image sequences given annotations from over 2.5 million users. Subsequently, in Part II, we develop visual sentiment and adjective-based semantics models from million-scale digital imagery mined from a social multimedia platform. Mid-level Representations for Visual Affect: While discrete semantic emotions and sentiment are classical representations of affect with decades of psychology grounding, the interdisciplinary nature of Affective Computing, now only about two decades old, allows for new avenues of representation. Mid-level representations have been proposed in numerous Computer Vision and Multimedia problems as an intermediary, and often more computable, step toward bridging the semantic gap between low-level system inputs and high-level label semantic abstractions. In Part II, inspired by this work, we adapt it for vision-based Affective Computing and adopt a semantic construct called adjective-noun pairs. Specifically, in Chapter 4, we explore the use of such adjective-noun pairs in the context of a social multimedia platform and develop a multilingual visual sentiment ontology with over 15,000 affective mid-level visual concepts across 12 languages associated with over 7.3 million images and representations from over 235 countries, resulting in the largest affective digital image corpus in both depth and breadth to date. In Chapter 5, we develop computational methods to predict such adjective-noun pairs and also explore their usefulness in traditional sentiment analysis but with a previously unexplored cross-lingual perspective. And in Chapter 6, we propose a new learning setting called 'cross-residual learning' building off recent successes in deep neural networks, and specifically, in residual learning; we show that cross-residual learning can be used effectively to jointly learn across even multiple related tasks in object detection (noun), more traditional affect modeling (adjectives), and affective mid-level representations (adjective-noun pairs), giving us a framework for better grounding the adjective-noun pair bridge in both vision and affect simultaneously

A crowdsourcing recommendation model for image annotations in cultural heritage platforms

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.Agencia Estatal de Investigación | Ref. TIN2017-87604-RXunta de Galicia | Ref. ED431B 2020/3

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies