DeepAI AI Chat
Log In Sign Up

A Graph-Based Neural Model for End-to-End Frame Semantic Parsing

09/25/2021
by   Zhichao Lin, et al.
0

Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/18/2022

A Double-Graph Based Framework for Frame Semantic Parsing

Frame semantic parsing is a fundamental NLP task, which consists of thre...
10/19/2017

SLING: A framework for frame semantic parsing

We describe SLING, a framework for parsing natural language into semanti...
01/02/2021

End-to-end Semantic Role Labeling with Neural Transition-based Model

End-to-end semantic role labeling (SRL) has been received increasing int...
10/15/2019

Text2Math: End-to-end Parsing Text into Math Expressions

We propose Text2Math, a model for semantically parsing text into math ex...
11/08/2022

Strictly Breadth-First AMR Parsing

AMR parsing is the task that maps a sentence to an AMR semantic graph au...
12/18/2022

A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset

Most recent semantic frame parsing systems for spoken language understan...
12/19/2018

Semantic Frame Parsing for Information Extraction : the CALOR corpus

This paper presents a publicly available corpus of French encyclopedic h...