Linköping Electronic Conference Proceedings
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The CLARIN:EL infrastructure: Platform, Portal, K-Centre
Get PDFThis paper presents the CLARIN:EL infrastructure, which comprises three pillars: the language resources and technologies Platform, the Portal and the Knowledge Centre. It serves as a com-prehensive and interoperable environment that supports language-related research in the fields of language technology, language studies, digital humanities, and political and social sciences. The Platform facilitates deposition, curation and sharing of digital language resources (catering for providers’ needs), and access to and automatic processing of these resources (catering for con-sumers’ needs). The Portal offers informative material about CLARIN:EL and support services to the community, including dissemination, awareness raising and training activities. The Knowledge Centre promotes digital literacy in the scientific domains served, by providing infor-mation on studies, educational and training material and publications. This paper discusses the CLARIN:EL pillars, the technical architecture, its design and implementation principles, the functionalities offered to the users, the support activities provided, usage analytics and future steps
Domain-Specific Languages for Epigraphy: the Case of ItAnt
Get PDFThis contribution illustrates how the definition of a Domain-Specific Language can support the activities of epigraphists and historical linguists. It presents and discusses a method and technological solution, based on Domain-Specific Languages, for facilitating scholars in digitally representing the available knowledge of archaic languages and cultures. This is achieved by increasing the human readability of the encoded data without sacrificing compliance with standard models and formats. The work is framed within the context of an Italian National collaborative research project devoted to the study of the languages and cultures of ancient Italy. The platform developed within this project offers an interesting use case and motivation for experimenting with Domain-Specific Languages for the creation of necessary digital critical editions of the inscriptions relevant for these languages. After explaining the definition process of the DSL grammar, we finally test the applicability of the DSL grammar to five example inscriptions in the Faliscan language
Heat Consumer Model for Robust and Fast Simulations of District Heating Networks
Get PDFDynamic thermo-hydraulic simulations of district heating networks are an essential tool to investigate concepts for their sustainable design and operation. The way the numerous heat consumers are modeled has crucial impact on the simulation performance. The proposed model for heat consumers is designed to require low computational effort by using a simplified modeling approach, avoiding state events and limiting its dynamics, while still reproducing their main characteristics. It is tested for a demonstration network, showing its ability to yield realistic results throughout the whole range of operational states including undersupply situations. The results show that the heat consumer model itself requires little time to simulate but still significantly influences the simulation time. Fast dynamics and including a bypass in the model increase the simulation time, so that users should sensibly choose how to use these options. Furthermore, heat consumer models triggering unnecessary state events result in the highest computational effort
Investigating Acoustic Correlates of Whisper Scoring for L2 Speech Using Forced alignment with the Italian Component of the ISLE corpus
Get PDFThis paper analyses how global phonetic analyses of learner data can be used to confirm Whisper probability scores assigned to learner phonetic data. We explore the Italian component of the ISLE corpus with phonetic analyses of 23 learners of English. Using a C++ wrapper of the Whisper models, we investigate the probability scores assigned by Whisper's tiny model. We discuss the phonetic features that may account for these Whisper predictions using P2FA-forced alignment. We try to correlate the quality of the phonetic realisation (measured using Levenshtein distance to the read text) to global vocalic measurements such as the convex hull or Euclidian distances between monophthongs. We show that Levenshtein distance to the reference transcription of the Whisper tidy model correlates with the grades assigned by the annotators and partially to the accuracy of the classification of monophthongs using the k-NN algorithm
Developing a Pedagogically Oriented Interactive Reading Tool with Teachers in the Loops
Get PDFReading is crucial for students' academic success and essential life skills. Particularly, the need for students to read in L2 English has grown due to its global significance. However, L2 readers often have limited opportunities for meaningful, interactive reading practice with immediate support. This paper introduces System A (Anonymized), a pedagogically oriented, web-based ICALL system designed to enhance L2 reading experiences, developed through an action research design involving teachers. System A offers a range of interactive features for students, including not only the autonomous identification of vocabulary and more than 650 grammar constructs, but making them interactively explorable in the text, providing detailed explanations and practical examples in contexts. To support effective teaching, System A employs a LLM for generating tailored reading comprehension questions and answer evaluation, with teachers actively involved. We present the development and application of the system from both technical and pedagogical perspectives to advance L2 learning research and refine educational tools
Evaluating the Generalisation of an Artificial Learner
Get PDFThis paper focuses on the creation of LLM-based artificial learners. Motivated by the capability of language models to encode language representation, we evaluate such models in predicting masked tokens in learner corpora. We pre-trained two learner models, one in a training set of the EFCAMDAT (natural learner model) and another in the C4200m dataset (syntehtic learner model), evaluating them against a native model using an external corpora of English for Specific purposes corpus of French undergraduates (CELVA) as test set. We measured metrics related to accuracy, consistency and divergence. While the native model performs reasonably well, the natural learner pre-trained model show improvements token in recall at k. We complement the accuracy metric showing that the native language model make "over-confident" mistakes where our artificial learners make mistakes where probabilities are uniform. Finally we show that the general tokens choices from the native model diverges from the natural learner model and that this divergence is higher on lower proficiency levels
Evaluating Space Creation in the National Hockey League using Puck and Player Tracking Data
No full textStar ice hockey players are often described as having a magnetic pull, with the ability to draw out opponents and generate dangerous opportunities for their linemates in the space left vacant by defenders. Using spatiotemporal Puck and Player Tracking (PPT) data, we develop a quantitative approach to measure how players create space while in possession of the puck, termed On-Puck Space Generation (OPSG). The benefits of our model’s approach include its decomposition into three components: 1) Rink Control, the probability of controlling the puck at a given location; 2) Rink Value, the probability of scoring from a given location; and 3) Transition Probability, the probability that the next on-puck event will occur at a given location. Preliminary results of our metric show that players who achieve high levels of OPSG are more likely to lead their team in goals, assists and points. Our model can be used to analyze which players are in positions of danger, identify instances in which an individual created valuable space for their teammates, and understand which teams are best at generating space
Puck Possessions and Team Success in the NHL
No full textThis paper investigates the relationship between puck possession and team success in the NHL, focusing on the games played during the 2023-2024 regular season (up to the All-Star break). The analysis first reveals a moderate correlation (r = 0.56) between average team possession percentage and Average Goal Differential (Avg. GoalDiff). Next, we introduce Average Offensive Zone Possession Time Differential (Avg. OZPTD) as a key metric, defined as the difference between a team’s offensive zone possession time and that of their opponents. We find a strong correlation (r = 0.77) between Avg. OZPTD and Avg. GoalDiff, thereby highlighting its relevance in assessing team performance. Our analysis confirms OZPTD’s stability, discriminatory power, and independence from existing metrics like Shot Attempt Percentage (SAT%), also known as Corsi. Additionally, we detail a comprehensive methodology for processing and cleaning possession data sourced from the NHL. This methodology underpins our findings and facilitates future research involving player and team possession data
The DIGARV Platform: A collaborative platform for working with cultural heritage data and research data
Get PDFThis article covers an easy-to-use research tool for collaborative work. The tool has been adapted for structured data and high-resolution images within four research projects at GRIDH. The platform is especially designed for working with temporal and spatial data. Furthermore, the platform gives researchers access to a relational database system through input forms and access to external cultural heritage data including high-resolution images. This way the platform also aims to utilize external data published as Linked Open Data (LOD) and, at the same time, prepare its own research data for publishing as LOD. Because of the spatial and temporal nature of the data, it is visualized in time and space through maps and timelines to give overview and context during the data management phase
Mind the Ownership Gap? Copyright in AI-generated Language Data
Get PDFFor language scientists, a prima facie advantage of AI-generated data over human-created content is that AI outputs are generally regarded as free from copyright. This contribution addresses this issue in some detail