A Double-Graph Based Framework for Frame Semantic Parsing

• URL: http://arxiv.org/abs/2206.09158v1
• Date: Sat, 18 Jun 2022 09:39:38 GMT
• Title: A Double-Graph Based Framework for Frame Semantic Parsing
• Authors: Ce Zheng, Xudong Chen, Runxin Xu, Baobao Chang
• Abstract summary: Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge. In this paper, we propose a Knowledge-guided semanticPK with Double-graph (KID) Our experiments show KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets.
• Score: 23.552054033442545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID). We first introduce Frame Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs (Frame Elements) built on the frame knowledge so that we can derive knowledge-enhanced representations for frames and FEs. Besides, we propose Frame Semantic Graph (FSG) to represent frame semantic structures extracted from the text with graph structures. In this way, we can transform frame semantic parsing into an incremental graph construction problem to strengthen interactions between subtasks and relations between arguments. Our experiments show that KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets. Our code is availavle at https://github.com/PKUnlp-icler/KID.

Related papers

• Image2Struct: Benchmarking Structure Extraction for Vision-Language Models [57.531922659664296]
  Image2Struct is a benchmark to evaluate vision-pixel models (VLMs) on extracting structure from images. In Image2Struct, VLMs are prompted to generate the underlying structure from an input image. The structure is then rendered to produce an output image, which is compared against the input image to produce a similarity score.
  arXiv  Detail & Related papers  (2024-10-29T18:44:59Z)
• Modeling Unified Semantic Discourse Structure for High-quality Headline Generation [45.23071138765902]
  We propose using a unified semantic discourse structure (S3) to represent document semantics. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.
  arXiv  Detail & Related papers  (2024-03-23T09:18:53Z)
• FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines [3.3181276611945263]
  We present FrameFinder, an open tool for extracting and analyzing frames in textual data. By analyzing the well-established gun violence frame corpus, we demonstrate the merits of our proposed solution.
  arXiv  Detail & Related papers  (2023-12-14T14:41:37Z)
• Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling [43.58108941071302]
  Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame roles defined in FrameNet. We propose a query-based framework named ArGument Extractor with Definitions in FrameNet (AGED) to mitigate these problems.
  arXiv  Detail & Related papers  (2022-12-05T05:09:12Z)
• Correspondence Matters for Video Referring Expression Comprehension [64.60046797561455]
  Video Referring Expression (REC) aims to localize the referent objects described in the sentence to visual regions in the video frames. Existing methods suffer from two problems: 1) inconsistent localization results across video frames; 2) confusion between the referent and contextual objects. We propose a novel Dual Correspondence Network (dubbed as DCNet) which explicitly enhances the dense associations in both the inter-frame and cross-modal manners.
  arXiv  Detail & Related papers  (2022-07-21T10:31:39Z)
• Two-stream Hierarchical Similarity Reasoning for Image-text Matching [66.43071159630006]
  A hierarchical similarity reasoning module is proposed to automatically extract context information. Previous approaches only consider learning single-stream similarity alignment. A two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation.
  arXiv  Detail & Related papers  (2022-03-10T12:56:10Z)
• One-shot Scene Graph Generation [130.57405850346836]
  We propose Multiple Structured Knowledge (Relational Knowledgesense Knowledge) for the one-shot scene graph generation task. Our method significantly outperforms existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-02-22T11:32:59Z)
• Transformer-based Dual Relation Graph for Multi-label Image Recognition [56.12543717723385]
  We propose a novel Transformer-based Dual Relation learning framework. We explore two aspects of correlation, i.e., structural relation graph and semantic relation graph. Our approach achieves new state-of-the-art on two popular multi-label recognition benchmarks.
  arXiv  Detail & Related papers  (2021-10-10T07:14:52Z)
• A Graph-Based Neural Model for End-to-End Frame Semantic Parsing [12.43480002133656]
  We propose an end-to-end neural model to tackle the frame semantic parsing task jointly. We exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive.
  arXiv  Detail & Related papers  (2021-09-25T08:54:33Z)
• Sister Help: Data Augmentation for Frame-Semantic Role Labeling [9.62264668211579]
  We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation.
  arXiv  Detail & Related papers  (2021-09-16T05:15:29Z)

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.