Extracting Relevance and Affect Information from Physiological Text Annotation

We present physiological text annotation, which refers to the practice of associating physiological responses to text content in order to infer characteristics of the user information needs and affective responses. Text annotation is a laborious task, and implicit feedback has been studied as a way to collect annotations without requiring any explicit action from the user. Previous work has explored behavioral signals, such as clicks or dwell time to automatically infer annotations, and physiological signals have mostly been explored for image or video content. We report on two experiments in which physiological text annotation is studied first to 1) indicate perceived relevance and then to 2) indicate affective responses of the users. The first experiment tackles the user’s perception of relevance of an information item, which is fundamental towards revealing the user’s information needs. The second experiment is then aimed at revealing the user’s affective responses towards a -relevant- text document. Results show that physiological user signals are associated with relevance and affect. In particular, electrodermal activity (EDA) was found to be different when users read relevant content than when they read irrelevant content and was found to be lower when reading texts with negative emotional content than when reading texts with neutral content. Together, the experiments show that physiological text annotation can provide valuable implicit inputs for personalized systems. We discuss how our findings help design personalized systems that can annotate digital content using human physiology without the need for any explicit user interaction

Assessing the Impact of Vocabulary Similarity on Multilingual Information Retrieval for Bantu Languages

Despite the availability of massive open information and efforts to promote multilingualism on the Web, content in Bantu languages remains negligible. Additionally, Information Retrieval (IR) systems, such as the Google search engine, use algorithms that work well with languages that have the most content. Similarities across related languages such as vocabulary overlap can potentially be exploited to provide more opportunities for information access for languages with limited digital content. This study investigates how vocabulary similarity impacts on the quality of search results in Multilingual Information Retrieval (MLIR) environments. More specifically, the study evaluates indexing strategies for MLIR and their effect on the quality of retrieval for related languages. A multilingual test collection consisting of two Bantu languages, Citumbuka and Chichewa, and English was developed and used in the evaluation. The results show that when comparing related and unrelated language pairs, MLIR indexing strategies result in comparable or worse retrieval performance

Looking for books in social media: An analysis of complex search requests

Contains fulltext : 140039.pdf (publisher's version ) (Open Access)Real-world information needs are generally complex, yet almost all research focuses on either relatively simple search based on queries or recommendation based on profiles. It is difficult to gain insight into complex information needs from observational studies with existing systems; potentially complex needs are obscured by the systems' limitations. In this paper we study explicit information requests in social media, focusing on the rich area of social book search. We analyse a large set of annotated book requests from the LibraryThing discussion forums. We investigate 1) the comprehensiveness of book requests on the forums, 2) what relevance aspects are expressed in real-world book search requests, and 3) how different types of search topics are related to types of users, human recommendations, and results returned by retrieval and recommender systems. We find that book search requests combine search and recommendation aspects in intricate ways that require more than only traditional search or (hybrid) recommendation approaches.37th European Conference on Information Retrieval, 29 maart 201

Cumulated Relative Position: A Metric for Ranking Evaluation

The development of multilingual and multimedia informa- tion access systems calls for proper evaluation methodologies to ensure that they meet the expected user requirements and provide the desired effectiveness. In this paper, we propose a new metric for ranking evaluation, the CRP