Improving nominal SRL in Chinese language with verbal SRL information and automatic predicate recognition

Li, Junhui; Zhou, Guodong; Zhao, Hai; Zhu, Qiaoming; Qian, Peng

doi:10.3115/1699648.1699674

Cited by 19 publications

(8 citation statements)

References 16 publications

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“…Semantic role labeling was pioneered by Gildea and Jurafsky (2002), also known as shallow semantic parsing. In early works of SRL, considerable attention has been paid to feature engineering (Pradhan et al, 2005;Zhao and Kit, 2008;Zhao et al, 2009a,b,c;Li et al, 2009;Björkelund et al, 2009;Zhao et al, 2013). Along with the the impressive success of deep neural networks Cai and Zhao, 2016;Wang et al, 2016b,a;, a series of neural SRL systems have been proposed.…”

Section: Related Workmentioning

confidence: 99%

A Unified Syntax-aware Framework for Semantic Role Labeling

Li¹,

He²,

Cai³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.

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Section: Related Workmentioning

confidence: 99%

A Unified Syntax-aware Framework for Semantic Role Labeling

Li¹,

He²,

Cai³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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“…Here, we compare our method with SourceLabeler system. We divide all sentences of Brown test set into six groups [1][2][3][4][5], [6][7][8][9][10], [11][12][13][14][15][16][17][18][19][20], [21][22][23][24][25][26][27][28][29][30], [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] and [51-]. The statistics about different groups are shown in Table 2.…”

Section: Effects Of the Length Of The Sentencesmentioning

confidence: 99%

“…The statistics about different groups are shown in Table 2. Most sentences fall in the groups [6][7][8][9][10], [11][12][13][14][15][16][17][18][19][20] and [21][22][23][24][25][26][27][28][29][30]. Figure 3 shows the performance comparisons of the Table 2 The statistics about different groups of Brown test set.…”

Section: Effects Of the Length Of The Sentencesmentioning

confidence: 99%

Adversarial Domain Adaptation Network for Semantic Role Classification

Yang

Zhou

et al. 2019

IEICE Trans. Inf. & Syst.

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In this paper, we study domain adaptation of semantic role classification. Most systems utilize the supervised method for semantic role classification. But, these methods often suffer severe performance drops on out-of-domain test data. The reason for the performance drops is that there are giant feature differences between source and target domain. This paper proposes a framework called Adversarial Domain Adaption Network (ADAN) to relieve domain adaption of semantic role classification. The idea behind our method is that the proposed framework can derive domaininvariant features via adversarial learning and narrow down the gap between source and target feature space. To evaluate our method, we conduct experiments on English portion in the CoNLL 2009 shared task. Experimental results show that our method can largely reduce the performance drop on out-of-domain test data.

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“…There were several LSTM models that also achieved high accuracy gains in Chinese SRL (Wang et al, 2015;Roth and Lapata, 2016;Sha et al, 2016;Marcheggiani et al, 2017;Qian et al, 2017). For event-nouns, Li et al (2009) showed that combining effective features in verbal SRL with nominal SRL can improve results. Although the authors did not demonstrate that verbal SRL also improves performance in combination with nominal SRL, we show that our model improves performance in both PASA and ENASA.…”

Section: Semantic Role Labelingmentioning

confidence: 99%

Multi-Task Learning for

Omori

Komachi

2019

Proceedings of the 2019 Conference of the North

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An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered stateof-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and eventnoun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multitask models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.

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Improving nominal SRL in Chinese language with verbal SRL information and automatic predicate recognition

Cited by 19 publications

References 16 publications

A Unified Syntax-aware Framework for Semantic Role Labeling

A Unified Syntax-aware Framework for Semantic Role Labeling

Adversarial Domain Adaptation Network for Semantic Role Classification

Multi-Task Learning for

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