The task of text detoxification aims to re-write toxic text into non-toxic text. Though existing methods have achieved impressive detoxification performance in monolingual settings, multilingual text detoxification re...
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Conventional Knowledge graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to d...
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ISBN:
(纸本)9798891760615
Conventional Knowledge graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to dynamic scenarios or domains, whereas a new type of knowledge emerges. This necessitates a system that can handle evolving schema automatically to extract information for KGC. To address this need, we propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training. We first split and convert existing datasets based on three principles to build a benchmark, i.e., horizontal schema expansion, vertical schema expansion, and hybrid schema expansion;then investigate the schema-adaptable performance of several well-known approaches such as Text2Event, TANL, UIE and GPT-3.5. We further propose a simple yet effective baseline dubbed ADAKGC, which contains schema-enriched prefix instructor and schema-conditioned dynamic decoding to better handle evolving schema. Comprehensive experimental results illustrate that ADAKGC can outperform baselines but still have room for improvement. We hope the proposed work can deliver benefits to the community(1).
Information extraction on documents still remains a challenge, especially when dealing with unstructured documents with complex and variable layouts. graph Neural Networks seem to be a promising approach to overcome t...
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The proceedings contain 23 papers. The topics discussed include: graph-based and graph-supported machine learning and deep learning methods;graph-based and graph-supported deep learning (e.g., graph-based recurrent an...
ISBN:
(纸本)9781950737864
The proceedings contain 23 papers. The topics discussed include: graph-based and graph-supported machine learning and deep learning methods;graph-based and graph-supported deep learning (e.g., graph-based recurrent and recursive networks);exploration of capabilities and limitations of graph-basedmethods being applied to neural networks;graph-based techniques for text summarization, simplification, and paraphrasing;graph-based techniques for document navigation and visualization;and using graphs-basedmethods to populate ontologies using textual data.
This paper focuses on the task of word sense disambiguation (WSD) on lexicographic examples relying on the French Lexical Network (fr-LN). For this purpose, we exploit the lexical and relational properties of the netw...
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Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. Manual annotation of such datasets, many of which are based on existing knowledge bases, is expens...
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Skeleton-based action recognition has rapidly become one of the most popular and essential research topics in computer vision. The task is to analyze the characteristics of human joints and accurately clas-sify their ...
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Skeleton-based action recognition has rapidly become one of the most popular and essential research topics in computer vision. The task is to analyze the characteristics of human joints and accurately clas-sify their behaviors through deep learning technology. Skeleton provides numerous unique advantages over other data modalities, such as robustness, compactness, noise immunity, etc. In particular, the skele-ton modality is extremely lightweight, which is especially beneficial for deep learning research in low -resource environments. Due to the non-European nature of skeleton data, graph Convolution Network (GCN) has become mainstream in the past few years, leveraging the benefits of processing topological information. However, with the explosive development of transformer methods in naturallanguage pro-cessing and computer vision, many works have applied transformer into the field of skeleton action recognition, breaking the accuracy monopoly of GCN. Therefore, we conduct a survey using transformer method for skeleton-based action recognition, forming of a taxonomy on existing works. This paper gives a comprehensive overview of the recent transformer techniques for skeleton action recognition, proposes a taxonomy of transformer-style techniques for action recognition, conducts a detailed study on bench-mark datasets, compares the algorithm accuracy of standard methods, and finally discusses the future research directions and trends. To the best of our knowledge, this study is the first to describe skeleton-based action recognition techniques in the style of transformers and to suggest novel recogni-tion taxonomies in a review. We are confident that Transformer-based action recognition technology will become mainstream in the near future, so this survey aims to help researchers systematically learn core tasks, select appropriate datasets, understand current challenges, and select promising future directions.(c) 2023 Elsevier B.V. All rights reserved.
graphs are widely used to model interconnected entities and improve downstream predictions in various real-world applications. However, real-world graphs nowadays are often associated with complex attributes on multip...
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ISBN:
(纸本)9781713899921
graphs are widely used to model interconnected entities and improve downstream predictions in various real-world applications. However, real-world graphs nowadays are often associated with complex attributes on multiple types of nodes and even links that are hard to model uniformly, while the widely used graph neural networks (GNNs) often require sufficient training toward specific downstream predictions to achieve strong performance. In this work, we take a fundamentally different approach than GNNs, to simultaneously achieve deep joint modeling of complex attributes and flexible structures of real-world graphs and obtain unsupervised generic graph representations that are not limited to specific downstream predictions. Our framework, built on a natural integration of language models (LMs) and random walks (RWs), is straightforward, powerful and data-efficient. Specifically, we first perform attributed RWs on the graph and design an automated program to compose roughly meaningful textual sequences directly from the attributed RWs;then we fine-tune an LM using the RW-based textual sequences and extract embedding vectors from the LM, which encapsulates both attribute semantics and graph structures. In our experiments, we evaluate the learned node embeddings towards different downstream prediction tasks on multiple real-world attributed graph datasets and observe significant improvements over a comprehensive set of state-of-the-art unsupervised node embedding methods. We believe this work opens a door for more sophisticated technical designs and empirical evaluations toward the leverage of LMs for the modeling of real-world graphs.
Knowledge graphs are crucial for structuring and integrating large amounts of data, improving decision-making and data interoperability, especially in the healthcare domain. This PhD thesis aims to implement a unified...
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Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-basedmethods, vertex and edge weights are usually created by cal...
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