A Neural Citation Count Prediction Model based on Peer Review Text

Li, Siqing; Zhao, Wayne Xin; Yin, Eddy Jing; Wen, Ji-Rong

doi:10.18653/v1/d19-1497

Cited by 43 publications

(34 citation statements)

References 37 publications

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“…For example, based on the characteristics of highly cited papers, Yan et al applied a regression model to study the interesting citation count prediction [ 22 ]. Li et al use the comprehensive semantic representation of peer-reviewed data learning papers to establish a neural prediction model to improve the citation prediction performance [ 23 ]. Second, the prediction of rise and fall of the topic attracted many scholars.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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Computer science discipline includes many research fields, which mutually influence and promote each other’s development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Guan

et al. 2021

Computational Intelligence and Neuroscience

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“…Predicting Performance from Language Previous research in natural language processing has explored the connections between textual features and audience engagement in books (Ganjigunte Ashok et al, 2013;Maharjan et al, 2018), YouTube (Kleinberg et al, 2018), news (Naseri and Zamani, 2019), TED talks (Tanveer et al, 2018), and tweets (Tan et al, 2014;Lampos et al, 2014). Other works have modeled the relationship between text and various performance metrics such as movie quote memorability (Danescu-Niculescu-Mizil et al, 2012), forecasting ability (Zong et al, 2020), congressional bill survival (Yano et al, 2012), success of job interviews (Naim et al, 2016), and impact of academic papers (Yogatama et al, 2011;Li et al, 2019), in addition to the entire field of sentiment and opinion mining of data such as user reviews (Pang et al, 2002).…”

Section: Related Workmentioning

confidence: 99%

Modeling Language Usage and Listener Engagement in Podcasts

Reddy¹,

Lazarova²,

Yu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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While there is an abundance of popular writing targeted to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with listener engagement. In this paper, we investigate how various factors -vocabulary diversity, distinctiveness, emotion, and syntax, among others -correlate with engagement, based on analysis of the creators' written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in highengagement podcasts, corroborating some aspects, and adding new perspectives on others.

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“…Some recent work focuses on predicting the number of citations from the paper text augmented with review text. To do so, Li et al (2019) created a dataset of abstracts and reviews from the ICLR and NIPS conferences: 1739 abstracts with a total of 7171 reviews for ICLR and 384 abstracts with 1119 reviews for NIPS. Plank and van Dale (2019) collect a dataset of 3427 papers with 12260 reviews.…”

Section: Related Workmentioning

confidence: 99%

Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction

Wenniger

Dongen²,

Aedmaa³

et al. 2020

Proceedings of the First Workshop on Scholarly Document Processing

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Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the stateof-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the Peer-Read dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains. On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.

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A Neural Citation Count Prediction Model based on Peer Review Text

Cited by 43 publications

References 37 publications

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

Modeling Language Usage and Listener Engagement in Podcasts

Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction

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