Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of <query concept, anchor concept> pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学 Cyber-Systems 控制研究所）；Squirrel AI（松鼠AI）；Alibaba Group（阿里巴巴集团）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；Ant Group（蚂蚁集团）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；School of Computing, University of Connecticut（康涅狄格大学计算机学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Deep neural networks have seen enormous success in various real-world applications. Beyond their predictions as point estimates, increasing attention has been focused on quantifying the uncertainty of their predictions. In this review, we show that the uncertainty of deep neural networks is not only important in a sense of interpretability and transparency, but also crucial in further advancing their performance, particularly in learning systems seeking robustness and efficiency. We will generalize the definition of the uncertainty of deep neural networks to any number or vector that is associ…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concepts needed to be organized as taxonomies, researchers turn to automatically completion of an existing taxonomy with new concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion framework, which effectively leverages both semantic features and structural information in the existing taxonomy and offers a better representation of candidate position to boost the performance of taxonomy completion. Specific…
Fudan University（复旦大学）；Université de Montréal（蒙特利尔大学）；Meituan（美团）
Taxonomies have been widely used in various domains to underpin numerous applications. Specially, product taxonomies serve an essential role in the e-commerce domain for the recommendation, browsing, and query understanding. However, taxonomies need to constantly capture the newly emerged terms or concepts in e-commerce platforms to keep up-to-date, which is expensive and labor-intensive if it relies on manual maintenance and updates. Therefore, we target the taxonomy expansion task to attach new concepts to existing taxonomies automatically. In this paper, we present a self-supervised and use…
Université de Montréal（蒙特利尔大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims to find a position for a new term in an existing taxonomy to capture the emerging knowledge in the world and keep the taxonomy dynamically updated. Previous taxonomy expansion solutions neglect valuable information brought by the hierarchical structure and evaluate the correctness of merely an added edge, which downgrade the problem to node-pair scoring or mini-path classification. In this paper, we propose the Hierarchy Expansion Framework (HEF), which fu…
Georgia Institute of Technology（佐治亚理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Electronic Science and Technology of China（电子科技大学）
Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms. We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion. To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and …
Warsaw University of Technology（华沙理工大学）；Jagiellonian University（雅盖隆大学）；Institute of Applied Psychology（应用心理学研究所）
Recently introduced self-supervised methods for image representation learning provide on par or superior results to their fully supervised competitors, yet the corresponding efforts to explain the self-supervised approaches lag behind. Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing. The probing tasks require knowledge about semantic relationships between image parts. Hence, we propose a systematic approach to obtain analogs of natural language i…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-尚佩恩分校）；University of Washington（华盛顿大学）
Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks. One critical challenge is that, as data and business scope grow in real applications, existing taxonomies need to be expanded to incorporate new concepts. Previous works on taxonomy expansion process the new concepts independently and simultaneously, ignoring the potential relationships among them and the appropriate order of inserting operations. However, in reality, the new concepts tend to be mutually correlated and form local hypernym-hyponym structure…
A Survey on Self-Supervised Representation Learning
Technical University of Dortmund（多特蒙德工业大学）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Research Center Jülich（于利希研究中心）；Helmholtz AI（亥姆霍兹人工智能（Helmholtz AI））
Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科技学院）；Department of Computing, the Hong Kong Polytechnic University（香港理工大学计算学系）
National Institute of Technology Silchar（国立锡尔恰尔理工学院）
In the last decade or so, we have witnessed deep learning reinvigorating the machine learning field. It has solved many problems in the domains of computer vision, speech recognition, natural language processing, and various other tasks with state-of-the-art performance. The data is generally represented in the Euclidean space in these domains. Various other domains conform to non-Euclidean space, for which graph is an ideal representation. Graphs are suitable for representing the dependencies and interrelationships between various entities. Traditionally, handcrafted features for graphs are i…
From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning
Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382007, India（信息与通信技术系，技术学院，潘迪·德恩达尔能源大学，古吉拉特邦加尔各纳，382007，印度）
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Shaoxing K3i Technology Co. Ltd（绍兴K3i科技有限公司）；State Key Laboratory of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Southern California（南加利福尼亚大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a "seed" t…
Deep Learning for Video Anomaly Detection: A Review
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
Pattern Recognition Lab, DCIS, PIEAS（模式识别实验室，DCIS，PIEAS）；PIEAS Artificial Intelligence Center (PAIC)（PIEAS人工智能中心）；Center of Secure Cyber-Physical Security Systems, Khalifa University（安全网络物理安全系统中心，卡比拉大学）；IBM Research（IBM研究院）；Department of Computer Science（计算机科学系）；Air University（空军大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Kyung Hee University (Global Campus)（庆熙大学（全球校区））；Department of Electrical Engineering and Automation（电气工程与自动化系）；Aalto University（艾尔沃斯大学）；Faculty of Engineering and Green Technology（工程与绿色技术学院）；Universiti Tunku Abdul Rahman（陶布鲁克大学）；Computer Vision Department（计算机视觉部门）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Foundation for Advancement of Science and Technology (FAST)（科学与技术进步基金会）
IBM Research（IBM研究）；University of Copenhagen（哥本哈根大学）；University of Cambridge（剑桥大学）；Rochester Institute of Technology（罗切斯特理工学院）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究学院，哈马德·本·哈利法大学）
The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression. Recent work presented the OLID dataset, which follows a taxonomy for offensive language identification that provides meaningful information for understanding the type and the target of offensive messages. However, it is limited in size and it might be biased towards offensive language as it was collected using keywords. In this work, we present SOLID, an expanded dataset, where the tweets were collected in a more principled …
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Amazon（亚马逊）；Carnegie Mellon University（卡内基梅隆大学）
Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies, most of them in practice are maintained by humans, which is labor-intensive and difficult to scale. While taxonomy construction from scratch is considerably studied in the literature, how to effectively enrich existing incomplete taxonomies remains an open yet important research question. Taxonomy enrichment not only requires the robustness to deal with emerging terms but also the consistency between existing taxono…
Tuenti Technologies（Tuenti技术公司）；Universidad de Oviedo（奥维耶多大学）
Search engine logs store detailed information on Web users interactions. Thus, as more and more people use search engines on a daily basis, important trails of users common knowledge are being recorded in those files. Previous research has shown that it is possible to extract concept taxonomies from full text documents, while other scholars have proposed methods to obtain similar queries from query logs. We propose a mixture of both lines of research, that is, mining query logs not to find related queries nor query hierarchies, but actual term taxonomies that could be used to improve search en…
Florida Atlantic University（佛罗里达大西洋大学）；Nanjing University of Finance and Economics（南京财经大学）
Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic meaning to the text. In earlier research, image synthesis relied mainly on word to image correlation analysis combined with supervised methods to find best alignment of the visual content matching to the text. Recent progress in deep learning (DL) has brought a new set of unsupervised deep learning methods, particularly deep generative models which are able to generate realistic visual images using suitably trained n…
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
University of Chinese Academy of Sciences（中国科学院大学）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）
Knowledge Graphs (KGs) are ubiquitous structures for information storagein several real-world applications such as web search, e-commerce, social networks, and biology. Querying KGs remains a foundational and challenging problem due to their size and complexity. Promising approaches to tackle this problem include embedding the KG units (e.g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results. These approaches, however, fail to capture the hierarchical nature and semantic information of the entities present in the graph. …
National Institute of Technology Trichy（特里奇国立理工学院）
Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models are built on the top of transformers, self-supervised learning and transfer learning. Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks. These models provide good background knowledge to downstream tasks which avoids training of downstream models from scratch. In this comprehensive survey paper…
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 surro…
