Search Results for author: Qiaoming Zhu

Found 38 papers, 2 papers with code

A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing

no code implementations COLING 2022 Yaxin Fan, Peifeng Li, Fang Kong, Qiaoming Zhu

Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation.

Discourse Parsing Multi-Task Learning

基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster Chinese Events on Social Streams)

no code implementations CCL 2020 Hengrui Guo, Zhongqing Wang, Peifeng Li, Qiaoming Zhu

面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前, 事件聚类模型主要分为无监督模型和有监督模型。无监督模型聚类效果较差, 有监督模型依赖大量标注数据。基于此, 本文提出了一种半监督事件聚类模型(SemiEC), 该模型在小规模标注数据的基础上, 利用LSTM表征事件, 利用线性模型计算文本相似度, 进行增量聚类, 利用增量聚类产生的标注数据对模型再训练, 结束后对不确定样本再聚类。实验表明, SemiEC的性能相比其他模型均有所提高。

Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning

no code implementations COLING 2022 Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP).

Data Augmentation Machine Reading Comprehension +4

Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark

no code implementations24 May 2023 Feng Jiang, Weihao Liu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Haizhou Li

Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings.

Discourse Parsing

Multi-Granularity Prompts for Topic Shift Detection in Dialogue

no code implementations23 May 2023 Jiangyi Lin, Yaxin Fan, Xiaomin Chu, Peifeng Li, Qiaoming Zhu

The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change.

Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing

no code implementations23 May 2023 Feng Jiang, Longwang He, Peifeng Li, Qiaoming Zhu, Haizhou Li

Discourse parsing, the task of analyzing the internal rhetorical structure of texts, is a challenging problem in natural language processing.

Discourse Parsing Transfer Learning

A Hybrid Model of Classification and Generation for Spatial Relation Extraction

no code implementations COLING 2022 Feng Wang Peifeng Li, Qiaoming Zhu

Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information.

Natural Language Understanding Relation Extraction

Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading

no code implementations COLING 2020 Feng Jiang, Xiaomin Chu, Peifeng Li, Fang Kong, Qiaoming Zhu

Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English.

Discourse Parsing

Negative Focus Detection via Contextual Attention Mechanism

no code implementations IJCNLP 2019 Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw

For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.

Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese

no code implementations ACL 2019 Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.

Tensor Networks

Document-Level Event Factuality Identification via Adversarial Neural Network

no code implementations NAACL 2019 Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP).

MCDTB: A Macro-level Chinese Discourse TreeBank

no code implementations COLING 2018 Feng Jiang, Sheng Xu, Xiaomin Chu, Peifeng Li, Qiaoming Zhu, Guodong Zhou

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus.

Reading Comprehension

Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse

no code implementations COLING 2018 Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu

The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information.

Question Answering Text Matching

Stance Detection with Hierarchical Attention Network

no code implementations COLING 2018 Qingying Sun, Zhongqing Wang, Qiaoming Zhu, Guodong Zhou

In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information.

Feature Engineering Opinion Mining +1

Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

1 code implementation ACL 2018 Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu

Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent.

Event Detection Feature Engineering

Global Inference to Chinese Temporal Relation Extraction

no code implementations COLING 2016 Peifeng Li, Qiaoming Zhu, Guodong Zhou, Hongling Wang

Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences.

Question Answering Temporal Relation Extraction +1

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