Cross-language context-aware citation recommendation in scientific articles

Tang, Xuewei; Wan, Xiaojun; Zhang, Xun

doi:10.1145/2600428.2609564

Cited by 46 publications

(48 citation statements)

References 23 publications

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“…In addition to the paper plain text files and the references database, we also provide the citation contexts of all successfully resolved references extracted to a CSV file as well as a script to create custom exports. 17 For the provided CSV export, we set the citation context length to 3 sentences-the sentence containing the citation as well as the one before and after-as used by Tang et al (2014) and Huang et al (2015). Each line in an export CSV has the following columns: cited MAG ID, adjacent cited MAG IDs, citing MAG ID, cited arXiv ID, adjacent cited arXiv IDs, citing arXiv ID, text (see bottom of Listing 2).…”

Section: Results Formatmentioning

confidence: 99%

unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

Saier

Färber

2020

Scientometrics

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In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation contextbased document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers' plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

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Section: Results Formatmentioning

confidence: 99%

unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

Saier

Färber

2020

Scientometrics

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“…Huang et al [ 20 ] assumed that the appearance of cited papers was a particular language and represented the cited papers in unique IDs regarded as new “words.” The probability of citing a paper given a citation context is directly estimated by using a translation model. Tang et al [ 21 ] proposed a joint embedding model to learn a low-dimensional embedding space for both contexts and citations.…”

Section: Related Workmentioning

confidence: 99%

SVD-CNN: A Convolutional Neural Network Model with Orthogonal Constraints Based on SVD for Context-Aware Citation Recommendation

Tao

Shen

Zhu

et al. 2020

Computational Intelligence and Neuroscience

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Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrelation in the weight matrix influences semantic similarity, which is a difficult problem to solve. In this paper, we propose a novel context-aware citation recommendation approach that can essentially improve the orthogonality of the weight matrix and explore more accurate citation patterns. We quantitatively show that the various reference patterns in the paper have interactional features that can significantly affect link prediction. We conduct experiments on the CiteSeer datasets. The results show that our model is superior to baseline models in all metrics.

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“…In this work, we train a skip-gram model to bridge the vocabulary gap between context-article pairs. Previous work represents the documents with averaged vectors of words (Tang et al, 2014;Tan et al, 2015). However, this may lead to the loss of detailed information of the documents.…”

Section: Implicit Semantics For Varietymentioning

confidence: 99%

News Citation Recommendation with Implicit and Explicit Semantics

Peng

Liu

Lin

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. Due to the sparsity issue of news citations and the engineering difficulty in obtaining information on authors, we focus on content similarity-based methods instead of collaborative filtering-based approaches. In this paper, we explore word embedding (i.e., implicit semantics) and grounded entities (i.e., explicit semantics) to address the variety and ambiguity issues of language. We formulate the problem as a reranking task and integrate different similarity measures under the learning to rank framework. We evaluate our approach on a real-world dataset. The experimental results show the efficacy of our method.

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Cross-language context-aware citation recommendation in scientific articles

Cited by 46 publications

References 23 publications

unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

SVD-CNN: A Convolutional Neural Network Model with Orthogonal Constraints Based on SVD for Context-Aware Citation Recommendation

News Citation Recommendation with Implicit and Explicit Semantics

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