Improving Graph-Based Text Representations with Character and Word Level N-grams

• URL: http://arxiv.org/abs/2210.05999v1
• Date: Wed, 12 Oct 2022 08:07:54 GMT
• Title: Improving Graph-Based Text Representations with Character and Word Level N-grams
• Authors: Wenzhe Li and Nikolaos Aletras
• Abstract summary: We propose a new word-character text graph that combines word and character n-gram nodes together with document nodes. We also propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph.
• Score: 30.699644290131044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based text representation focuses on how text documents are represented as graphs for exploiting dependency information between tokens and documents within a corpus. Despite the increasing interest in graph representation learning, there is limited research in exploring new ways for graph-based text representation, which is important in downstream natural language processing tasks. In this paper, we first propose a new heterogeneous word-character text graph that combines word and character n-gram nodes together with document nodes, allowing us to better learn dependencies among these entities. Additionally, we propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph. Extensive experiments in text classification and automatic text summarization benchmarks demonstrate that our proposed models consistently outperform competitive baselines and state-of-the-art graph-based models.

Related papers

• Hierarchical Compression of Text-Rich Graphs via Large Language Models [63.75293588479027]
  Text-rich graphs are prevalent in data mining contexts like e-commerce and academic graphs. This paper introduces Hierarchical Compression'' (HiCom), a novel method to align the capabilities of LLMs with the structure of text-rich graphs. HiCom can outperform both GNNs and LLM backbones for node classification on e-commerce and citation graphs.
  arXiv  Detail & Related papers  (2024-06-13T07:24:46Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning [54.84870836443311]
  The paper presents a new paradigm for understanding and reasoning about graph data by integrating image encoding and multimodal technologies. This approach enables the comprehension of graph data through an instruction-response format, utilizing GPT-4V's advanced capabilities. The study evaluates this paradigm on various graph types, highlighting the model's strengths and weaknesses, particularly in Chinese OCR performance and complex reasoning tasks.
  arXiv  Detail & Related papers  (2023-12-16T08:14:11Z)
• Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models [14.251972223585765]
  This paper introduces a new approach to encoding a graph with diverse modalities, such as text, image, and motif, and prompts to approximate a graph's global connectivity. The study also presents GraphTMI, a novel benchmark for evaluating Large Language Models (LLMs) in graph structure analysis.
  arXiv  Detail & Related papers  (2023-11-16T12:45:41Z)
• Pretraining Language Models with Text-Attributed Heterogeneous Graphs [28.579509154284448]
  We present a new pretraining framework for Language Models (LMs) that explicitly considers the topological and heterogeneous information in Text-Attributed Heterogeneous Graphs (TAHGs) We propose a topology-aware pretraining task to predict nodes involved in the context graph by jointly optimizing an LM and an auxiliary heterogeneous graph neural network. We conduct link prediction and node classification tasks on three datasets from various domains.
  arXiv  Detail & Related papers  (2023-10-19T08:41:21Z)
• ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings [20.25180279903009]
  We propose Contrastive Graph-Text pretraining (ConGraT) for jointly learning separate representations of texts and nodes in a text-attributed graph (TAG) Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. Experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling.
  arXiv  Detail & Related papers  (2023-05-23T17:53:30Z)
• Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification [60.233529926965836]
  We propose a new method called SHINE, which is based on graph neural network (GNN) for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts.
  arXiv  Detail & Related papers  (2021-10-30T05:33:05Z)
• JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs [44.06715423776722]
  We propose a graph-text joint representation learning model called JointGT. During encoding, we devise a structure-aware semantic aggregation module which is plugged into each Transformer layer. We show that JointGT obtains new state-of-the-art performance on various KG-to-text datasets.
  arXiv  Detail & Related papers  (2021-06-19T14:10:10Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching [24.02847802702168]
  This paper proposes a graph-based approach for sentence matching. We represent a sentence pair as a graph with several carefully design strategies. We then employ a novel gated graph attention network to encode the constructed graph for sentence matching.
  arXiv  Detail & Related papers  (2020-10-15T11:25:54Z)
• Improving Image Captioning with Better Use of Captions [65.39641077768488]
  We present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences.
  arXiv  Detail & Related papers  (2020-06-21T14:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.