Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. In this paper, we focus on automatic detection of one-sentence definitions in mathematical texts, which are difficult to separate from surrounding text. We experiment with several data representations, which include sentence syntactic structure and word embeddings, and apply deep learning methods such as the Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM), in order to identify mathematical definitions. Our experiments demonstrate the superiority of CNN and its combination with LSTM, when applied on the syntactically-enriched input representation. We also present a new dataset for definition extraction from mathematical texts. We demonstrate that this dataset is beneficial for training supervised models aimed at extraction of mathematical definitions. Our experiments with different domains demonstrate that mathematical definitions require special treatment, and that using cross-domain learning is inefficient for that task.
Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries
Nara Institute of Science and Technology（奈良科学技术大学）；RIKEN（日本研究机构）；Resolve Research（Resolve研究）；The University of Tokyo（东京大学）；Tohoku University（东北大学）；Serpenti Sei Japan（日本Serpenti Sei）
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
College of Intelligence and Computing, Tianjin University（天津大学智能计算学院）；Tianjin Key Laboratory of Healthy Habitat and Smart Technology（天津健康人居环境与智能技术重点实验室）；Laboratory of Computation and Analytics of Complex Management Systems, Tianjin University（复杂管理系统计算与分析实验室）
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL
Shanghai University of Finance and Economics（上海财经大学）；Beihang University（北京航空航天大学）；Deepexi Technology Co. Ltd.（深圳市智元机器有限公司）；Southern University of Science and Technology（南方科技大学）
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Allen Institute for Artificial Intelligence（艾伦人工智能研究院）；University of Washington（华盛顿大学）；University of Arizona（亚利桑那大学）；Hebrew University of Jerusalem（耶路撒冷希伯来大学）
Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy…
Towards Automated Error Discovery: A Study in Conversational AI
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt（德累斯顿技术大学）
