arXiv subjects

Marco Cremaschi

Publications and source records attributed to Marco Cremaschi.

3 recordsLinked to original sources

arXiv2024

Survey on Semantic Interpretation of Tabular Data: Challenges and Directions

Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.

cs.AI↗

arXiv2024

Evaluating LLMs on Entity Disambiguation in Tables

Tables are crucial containers of information, but understanding their meaning may be challenging. Over the years, there has been a surge in interest in data-driven approaches based on deep learning that have increasingly been combined with heuristic-based ones. In the last period, the advent of \acf{llms} has led to a new category of approaches for table annotation. However, these approaches have not been consistently evaluated on a common ground, making evaluation and comparison difficult. This work proposes an extensive evaluation of four STI SOTA approaches: Alligator (formerly s-elbat), Dagobah, TURL, and TableLlama; the first two belong to the family of heuristic-based algorithms, while the others are respectively encoder-only and decoder-only Large Language Models (LLMs). We also include in the evaluation both GPT-4o and GPT-4o-mini, since they excel in various public benchmarks. The primary objective is to measure the ability of these approaches to solve the entity disambiguation task with respect to both the performance achieved on a common-ground evaluation setting and the computational and cost requirements involved, with the ultimate aim of charting new research paths in the field.

cs.CL↗

arXiv2023

An experimental protocol to access immersiveness in video games

In the video game industry, great importance is given to the experience that the user has while playing a game. In particular, this experience benefits from the players' perceived sense of being in the game or immersion. The level of user immersion depends not only on the game's content but also on how the game is displayed, thus on its User Interface (UI) and the Head's-Up Display (HUD). Another factor influencing immersiveness that has been found in the literature is the player's expertise: the more experience the user has with a specific game, the less they need information on the screen to be immersed in the game. Player's level of immersion can be accessed by using both questionnaires of their perceived experience and exploiting their behavioural and physiological responses while playing the target game. Therefore, in this paper, we propose an experimental protocol to access immersiveness of gamers while playing a third-person shooter (Fortnite) with UIs with a standard, a dietetic, and a proposed HUD. A subjective evaluation of the immersion will be provided by completing the Immersive Experience Questionnaire (IEQ), while objective indicators will be provided by face tracking, behaviour and physiological responses analyses. The ultimate goal of this study is to define guidelines for video game UI development that can enhance the players' immersion.

cs.HC↗