Event Detection and Co-reference with Minimal Supervision

By Haoruo Peng, Yangqiu Song, Dan Roth. 2016

113 citations recorded by the source corpus.

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Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Weizhu Chen
3. Xiaodong Liu
4. Mohit Bansal
5. Pengcheng He
6. Caiming Xiong
7. Shuohang Wang
8. Richard Socher

In Management Science and Operations Research

1. Heng Ji
2. Jiawei Han
3. Xiaodong Yu
4. Wenpeng Yin
5. Dan Roth
6. Jing Jiang
7. Yelong Shen
8. Jianfeng Gao

In Natural Language Processing

1. Jianfeng Gao
2. Weizhu Chen
3. Xiaodong Liu
4. Pengcheng He
5. Caiming Xiong
6. Richard Socher
7. Shuohang Wang
8. Samuel R. Bowman

Citations in the corpus

• A Hybrid Neural Network Model for Commonsense Reasoning (2019, background): “ution, where a machine (AI agent) must identify the antecedent of an ambiguous pronoun in a statement. WSC and PDP differ from other co-reference resolution tasks (Soon et al.,2001;Ng and Cardie,2002;Peng et al., 2016) in that commonsense knowledge, which cannot be explicitly decoded from the given ”
• Paired Representation Learning for Event and Entity Coreference (2020, methodology): “A even earlier work byChen et al.(2009) showed that adding arguments and event attributes improves the performance. Table7shows that, by only using gold triggers, we can achieve a very close number toPeng et al. (2016). It is worthing mentioning that our numbers and that byPeng et al.(2016) are not ”
• Document-Level Event Argument Extraction by Conditional Generation (2021, background): “Early attempts at zero-shot or few-shot event extraction rely on preprocessing such as semantic role labeling(SRL) (Peng et al., 2016) or abstract meaning representation (AMR) (Huang et al., 2018) to detect trigger mentions and argument mentions before performing classification on the detected spans.”
• Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (2019, background): “Although both tasks are based on pronoun disambiguation, a subtask of coreference resolution (Soon et al., 2001; Ng and Cardie, 2002; Peng et al., 2016), PDP and WSC differ from normal pronoun disambiguation due to their unique properties, which are based on commonsense, selecting the most likely an”
• Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism (2020, background): “The CBOW component infers the target word when given the context words, while the skip-gram component infers the context words when given an input word [11].”