.png){kind=icon}
返回
Improving in-text citation reason extraction and classification using supervised machine learning techniques
.png){kind=icon}
PRE
AI
DOI：10.1016/j.csl.2023.101526.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the last decade, automatic extraction and classification of in-text citations have received immense popularity and have become one of the most frequently used techniques to evaluate research. Due to the large volume of in-text citations in various digital libraries such as Web of Science, Scopus, Google Scholar, Microsoft Academic, etc., machine learning models and natural language processing techniques are being used to extract, classify, and analyze them. Typical automatic in-text classification techniques use sentiment-based classes (Positive, Negative, and Neutral). However, there are cognitive-based schemes as well that classify in-text citations based on the author's perspective. In such schemes, extracting citation reasons with high recall is challenging. To address this challenge, we have used eight citations' context and reason classes defined by CCRO (Citation's Context and Reasons Ontology) to develop a machine learning model to achieve high recall without compromising on precision. We have worked on Association for Computational Linguistics Corpus with over 7000 in-text citations, randomly annotated by experts in CCRO classes. Afterwards, an array of machine-learning models is implemented on the annotated dataset: Support Vector Machine (SVM), Naive Bayesian (NB), and Random Forest (RF). We have used various part-of-speech (Nouns, Verbs, Adverbs, and Adjectives) as novel features. Our results show that we have outperformed the three comparative models by achieving 91% accuracy.
Keyword:
Citation reason classification
Machine learning
Supervised learning
期刊
C
IF:
3.4
论文数:
1.5K
被引数:
2.6K
机构
引用论文
分享收藏Identification of important citations by exploiting research articles' metadata and cue-terms from content通过利用研究文章的元数据和内容中的提示词来识别重要引文.png)
SCIENTOMETRICS
SCIENTOMETRICSIF3.5
PREAI
分享收藏Impaired perceptual learning in a mouse model of Fragile X syndrome is mediated by parvalbumin neuron dysfunction and is reversible脆性X综合征小鼠模型的感知学习受损是由小白蛋白神经元功能障碍介导的，并且是可逆的
OAAI
OAAI分享收藏Occurrence of Furosine and Hydroxymethylfurfural in Breakfast Cereals. Evolution of the Spanish Market from 2006 to 2018FoodsOAAI
FoodsIF0
OAAI分享收藏分享收藏
查看更多内容