Improving Citation Polarity Classification with Product Reviews

Abstract

Recent work classifying citations in scientific literature has shown that it is possible to improve classification results with extensive feature engineering. While this result confirms that citation classification is feasible, there are two drawbacks to this approach: (i) it requires a large annotated corpus for supervised classification, which in the case of scientific literature is quite expensive; and (ii) feature engineering that is too specific to one area of scientific literature may not be portable to other domains, even within scientific literature. In this paper we address these two drawbacks. First, we frame citation classification as a domain adaptation task and leverage the abundant labeled data available in other domains. Then, to avoid over-engineering specific citation features for a particular scientific domain, we explore a deep learning neural network approach that has shown to generalize well across domains using unigram and bigram features. We achieve better citation classification results with this cross-domain approach than using in-domain classification.

Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

“…However, this is not consistent across all studies. For example, Jochim and Schütze ( 2014 ) find that combining marginalized stacked denoising autoencoders (mSDA) (Chen et al, 2012 ) and frustratingly easy domain adaptation (FEDA) (Daumé III, 2007 ) performs worse than individual methods in preliminary experiments on citation polarity classification. Both methods are feature-centric, though mSDA is a generalization method (FG) while FEDA is an augmentation method (FA).…”

“…Additionally, mSDA is an unlabeled adaptation method while FEDA is a labeled adaptation method. Owing to negative results, Jochim and Schütze ( 2014 ) do not experiment further to find a combination that might have worked. Wright and Augenstein ( 2020 ) show that combining adversarial domain adaptation (ADA) (Ganin and Lempitsky, 2015 ) with pretraining does not improve performance, but combining mixture of experts (MoE) with pretraining does.…”

Investigating Convolutional Networks and Domain-Specific Embeddings for Semantic Classification of Citations

“…Jochim and Schütze [11] rst applied a deep learning model to the citation polarity classi cation. In a domain-adaption se ing, they trained a marginalized stacked denoising autoencoders (mSDA) on product reviews and used it to predict the polarity of citations.…”

Sentiment Analysis of Citations Using Word2vec

“…For example, [1] extracted several features for citation purpose and polarity classification, such as reference count, contrary expression and dependency relations. Jochim et al tried to improve the result by using unigram and bigram features [2]. [3] used word level features, contextual polarity features, and sentence structure based features to detect sentiment citations.…”