Access to sign language data is far from adequate. We show that it is possible to collect the data from social networking services such as TikTok, Instagram, and YouTube by applying data filtering to enforce quality standards and by discovering patterns in the filtered data, making it easier to analyse and model. Using our data collection pipeline, we collect and examine the interpretation of songs in both the American Sign Language (ASL) and the Brazilian Sign Language (Libras). We explore their differences and similarities by looking at the co-dependence of the orientation and location phonological parameters
A Data Synthesis Method Driven by Large Language Models for Proactive Mining of Implicit User Intentions in Tourism
School of Computer & Communication Engineering, University of Science and Technology Beijing（信息与通信工程学院，北京科技大学）；School of Computer Science, University of South China（计算机科学学院，南方大学）；Department of Computer Science, Blekinge institute of Technology（计算机科学系，布莱京厄学院）
School of Computer Science, University of St Andrews（圣安德鲁斯大学计算机科学学院）；Center for Language and Communication Studies, Trinity College Dublin（都柏林圣三一学院语言与传播研究中心）
This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtain…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Amazon AI（亚马逊人工智能）
Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge f…
Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence …
Database Systems and Data Mining, LMU Munich（慕尼黑大学数据库系统与数据研究所）；LMU Munich（慕尼黑大学）
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question and it is vividly discussed across all major conferences. In this work, we propose an Argument Mining based approach for the assistance of editors, meta-reviewers, and reviewers. We demonstrate that the decision process in the field of sci…
RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Department of Pediatrics, University of Illinois College of Medicine Peoria（伊利诺伊大学皮奥里亚医学院儿科系）
Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers
Model-based language specification has applications in the implementation of language processors, the design of domain-specific languages, model-driven software development, data integration, text mining, natural language processing, and corpus-based induction of models. Model-based language specification decouples language design from language processing and, unlike traditional grammar-driven approaches, which constrain language designers to specific kinds of grammars, it needs general parser generators able to deal with ambiguities. In this paper, we propose Fence, an efficient bottom-up par…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google Research（谷歌研究院）；Army Research Laboratory（陆军研究实验室）
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. Recently, a few data-driven methods have been developed successfully for extraction of phrases from massive domain-specific text. However, none of the stat…
The problem of spatiotemporal event visualization based on reports entails subtasks ranging from named entity recognition to relationship extraction and mapping of events. We present an approach to event extraction that is driven by data mining and visualization goals, particularly thematic mapping and trend analysis. This paper focuses on bridging the information extraction and visualization tasks and investigates topic modeling approaches. We develop a static, finite topic model and examine the potential benefits and feasibility of extending this to dynamic topic modeling with a large number…
University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；University of Pisa（比萨大学）
In today's digital world, there is an increasing focus on soft skills. On the one hand, they facilitate innovation at companies, but on the other, they are unlikely to be automated soon. Researchers struggle with accurately approaching quantitatively the study of soft skills due to the lack of data-driven methods to retrieve them. This limits the possibility for psychologists and HR managers to understand the relation between humans and digitalisation. This paper presents SkillNER, a novel data-driven method for automatically extracting soft skills from text. It is a named entity recognition (…
Vienna University of Economics and Business（维也纳经济大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；University of Amsterdam（阿姆斯特丹大学）
Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model…
Given, When, Then, Again: Mining Subscenario Refactoring Candidates in Behaviour-Driven Test Suites with ML Classifiers and LLM-Judge Baselines
Independent Researcher（独立研究者；应用MBA（数据分析），德克萨斯韦斯利安大学）；Applied MBA (Data Analytics), Texas Wesleyan University（独立研究者；计算机工程学士，国立科学与技术大学（NUST））；Independent Researcher（独立研究者；管理硕士，慕尼黑技术大学）；B.E. Computer Engineering, National University of Sciences and Technology (NUST)；Independent Researcher；M.Sc. Management, Technical University of Munich
