Fang Kong


2020

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A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure
Longyin Zhang | Yuqing Xing | Fang Kong | Peifeng Li | Guodong Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years. However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse. In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing. On the basis, we propose a top-down neural architecture toward text-level DRS parsing. In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly. In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack. Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards text-level DRS parsing.

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融入对话上文整体信息的层次匹配回应选择(Learning Overall Dialogue Information for Dialogue Response Selection)
Bowen Si (司博文) | Fang Kong (孔芳)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

对话是一个顺序交互的过程,回应选择旨在根据已有对话上文选择合适的回应,是自然语言处理领域的研究热点。已有研究取得了一定的成功,但仍然存在两个突出的问题。一是现有的编码器在挖掘对话文本语义信息上尚存在不足;二是只考虑每一回合对话与备选回应之间的关系,忽视了对话上文的整体语义信息。针对问题一,本文借助多头自注意力机制有效捕捉对话文本的语义信息;针对问题二,整合对话上文的整体语义信息,分别从单词、句子以及整体对话上文三个层次与备选回应进行匹配,充分保证匹配信息的完整。在Ubuntu Corpus V1和Douban Conversation Corpus数据集上的对比实验表明了本文给出方法的有效性。

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面向微博文本的融合字词信息的轻量级命名实体识别(Lightweight Named Entity Recognition for Weibo Based on Word and Character)
Chun Chen (陈淳) | Mingyang Li (李明扬) | Fang Kong (孔芳)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

中文社交媒体命名实体识别由于其领域特殊性,一直广受关注。非正式且无结构的微博文本存在以下两个问题:一是词语边界模糊;二是语料规模有限。针对问题一,本文将同维度的字词进行融合,获得丰富的文本序列表征;针对问题二,提出了基于Star-Transformer框架的命名实体识别模型,借助星型拓扑结构更好地捕获动态特征;同时利用高速网络优化Star-Transformer中的信息桥接,提升模型的鲁棒性。本文提出的轻量级命名实体识别模型取得了目前Weibo语料上最好的效果。

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Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading
Feng Jiang | Xiaomin Chu | Peifeng Li | Fang Kong | Qiaoming Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from longer discourse units and fewer explicit connectives. To alleviate the above issues, we propose two reading modes, i.e., the global backward reading and the local reverse reading, to construct Chinese paragraph level discourse trees. The former processes discourse units from the end to the beginning in a document to utilize the left-branching bias of discourse structure in Chinese, while the latter reverses the position of paragraphs in a discourse unit to enhance the differentiation of coherence between adjacent discourse units. The experimental results on Chinese MCDTB demonstrate that our model outperforms all strong baselines.

2019

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Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
Sheng Xu | Peifeng Li | Fang Kong | Qiaoming Zhu | Guodong Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.

2016

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SoNLP-DP System for ConLL-2016 English Shallow Discourse Parsing
Fang Kong | Sheng Li | Junhui Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

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SoNLP-DP System for ConLL-2016 Chinese Shallow Discourse Parsing
Junhui Li | Fang Kong | Sheng Li | Muhua Zhu | Guodong Zhou
Proceedings of the CoNLL-16 shared task

2015

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The SoNLP-DP System in the CoNLL-2015 shared Task
Fang Kong | Sheng Li | Guodong Zhou
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

2014

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A Constituent-Based Approach to Argument Labeling with Joint Inference in Discourse Parsing
Fang Kong | Hwee Tou Ng | Guodong Zhou
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Building Chinese Discourse Corpus with Connective-driven Dependency Tree Structure
Yancui Li | Wenhe Feng | Jing Sun | Fang Kong | Guodong Zhou
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Exploiting Zero Pronouns to Improve Chinese Coreference Resolution
Fang Kong | Hwee Tou Ng
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Collective Personal Profile Summarization with Social Networks
Zhongqing Wang | Shoushan Li | Fang Kong | Guodong Zhou
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Exploring Local and Global Semantic Information for Event Pronoun Resolution
Fang Kong | Guodong Zhou
Proceedings of COLING 2012

2011

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Combining Dependency and Constituent-based Syntactic Information for Anaphoricity Determination in Coreference Resolution
Fang Kong | Guodong Zhou
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2010

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Dependency-driven Anaphoricity Determination for Coreference Resolution
Fang Kong | Guodong Zhou | Longhua Qian | Qiaoming Zhu
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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A Tree Kernel-Based Unified Framework for Chinese Zero Anaphora Resolution
Fang Kong | Guodong Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation
GuoDong Zhou | Fang Kong
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective
Fang Kong | GuoDong Zhou | Qiaoming Zhu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Semi-Supervised Learning for Semantic Relation Classification using Stratified Sampling Strategy
Longhua Qian | Guodong Zhou | Fang Kong | Qiaoming Zhu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Context-Sensitive Convolution Tree Kernel for Pronoun Resolution
GuoDong Zhou | Fang Kong | QiaoMing Zhu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Exploiting Constituent Dependencies for Tree Kernel-Based Semantic Relation Extraction
Longhua Qian | Guodong Zhou | Fang Kong | Qiaoming Zhu | Peide Qian
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)