Sentence Structure and Word Relationship Modeling for Emphasis Selection
- URL: http://arxiv.org/abs/2108.12750v1
- Date: Sun, 29 Aug 2021 04:43:25 GMT
- Title: Sentence Structure and Word Relationship Modeling for Emphasis Selection
- Authors: Haoran Yang and Wai Lam
- Abstract summary: Emphasis Selection is a newly proposed task which focuses on choosing words for emphasis in short sentences.
Traditional methods only consider the sequence information of a sentence while ignoring the rich sentence structure and word relationship information.
In this paper, we propose a new framework that considers sentence structure via a sentence structure graph and word relationship via a word similarity graph.
- Score: 33.71757542373714
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Emphasis Selection is a newly proposed task which focuses on choosing words
for emphasis in short sentences. Traditional methods only consider the sequence
information of a sentence while ignoring the rich sentence structure and word
relationship information. In this paper, we propose a new framework that
considers sentence structure via a sentence structure graph and word
relationship via a word similarity graph. The sentence structure graph is
derived from the parse tree of a sentence. The word similarity graph allows
nodes to share information with their neighbors since we argue that in emphasis
selection, similar words are more likely to be emphasized together. Graph
neural networks are employed to learn the representation of each node of these
two graphs. Experimental results demonstrate that our framework can achieve
superior performance.
Related papers
- Graph Neural Networks on Discriminative Graphs of Words [19.817473565906777]
In this work, we explore a new Discriminative Graph of Words Graph Neural Network (DGoW-GNN) approach to classify text.
We propose a new model for the graph-based classification of text, which combines a GNN and a sequence model.
We evaluate our approach on seven benchmark datasets and find that it is outperformed by several state-of-the-art baseline models.
arXiv Detail & Related papers (2024-10-27T15:14:06Z) - Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types.
We focus on models which are capable of interactively mapping user utterances into executable logical forms.
arXiv Detail & Related papers (2023-05-04T16:04:41Z) - Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented
Syntax Graph Pruning [39.76268402567324]
We propose a novel model termed Neural Subgraph Explorer.
It reduces the noisy information via pruning target-irrelevant nodes on the syntax graph.
It introduces beneficial first-order connections between the target and its related words into the obtained graph.
arXiv Detail & Related papers (2022-05-23T00:29:32Z) - Pruned Graph Neural Network for Short Story Ordering [0.7087237546722617]
Organizing sentences into an order that maximizes coherence is known as sentence ordering.
We propose a new method for constructing sentence-entity graphs of short stories to create the edges between sentences.
We also observe that replacing pronouns with their referring entities effectively encodes sentences in sentence-entity graphs.
arXiv Detail & Related papers (2022-03-13T22:25:17Z) - 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) - Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching.
The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k)
It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
arXiv Detail & Related papers (2021-09-01T08:24:02Z) - 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) - Embedding Words in Non-Vector Space with Unsupervised Graph Learning [33.51809615505692]
We introduce GraphGlove: unsupervised graph word representations which are learned end-to-end.
In our setting, each word is a node in a weighted graph and the distance between words is the shortest path distance between the corresponding nodes.
We show that our graph-based representations substantially outperform vector-based methods on word similarity and analogy tasks.
arXiv Detail & Related papers (2020-10-06T10:17:49Z) - Heterogeneous Graph Neural Networks for Extractive Document
Summarization [101.17980994606836]
Cross-sentence relations are a crucial step in extractive document summarization.
We present a graph-based neural network for extractive summarization (HeterSumGraph)
We introduce different types of nodes into graph-based neural networks for extractive document summarization.
arXiv Detail & Related papers (2020-04-26T14:38:11Z) - Iterative Context-Aware Graph Inference for Visual Dialog [126.016187323249]
We propose a novel Context-Aware Graph (CAG) neural network.
Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
arXiv Detail & Related papers (2020-04-05T13:09:37Z) - Bridging Knowledge Graphs to Generate Scene Graphs [49.69377653925448]
We propose a novel graph-based neural network that iteratively propagates information between the two graphs, as well as within each of them.
Our Graph Bridging Network, GB-Net, successively infers edges and nodes, allowing to simultaneously exploit and refine the rich, heterogeneous structure of the interconnected scene and commonsense graphs.
arXiv Detail & Related papers (2020-01-07T23:35:52Z)
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.