Citations are generally analyzed using only quantitative measures while
excluding qualitative aspects such as sentiment and intent. However,
qualitative aspects provide deeper insights into the impact of a scientific
research artifact and make it possible to focus on relevant literature free
from bias associated with quantitative aspects. Therefore, it is possible to
rank and categorize papers based on their sentiment and intent. For this
purpose, larger citation sentiment datasets are required. However, from a time
and cost perspective, curating a large citation sentiment dataset is a
challenging task. Particularly, citation sentiment analysis suffers from both
data scarcity and tremendous costs for dataset annotation. To overcome the
bottleneck of data scarcity in the citation analysis domain we explore the
impact of out-domain data during training to enhance the model performance. Our
results emphasize the use of different scheduling methods based on the use
case. We empirically found that a model trained using sequential data
scheduling is more suitable for domain-specific usecases. Conversely, shuffled
data feeding achieves better performance on a cross-domain task. Based on our
findings, we propose an end-to-end trainable multi-task model that covers the
sentiment and intent analysis that utilizes out-domain datasets to overcome the
data scarcity.

通过利用外部数据集，该文提出了一个端到端可训练的多任务模型，覆盖引文情感分析和意图分析，并且可以克服引文分析领域的数据稀缺性问题。

利用外领域数据集增强多任务引文分析

Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis

Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied to sentiment analysis of short texts. In this work, I
conducted empirical research with the question: how well does word2vec work on
the sentiment analysis of citations? The proposed method constructed sentence
vectors (sent2vec) by averaging the word embeddings, which were learned from
Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
word embeddings (PS-Embeddings) for classifying positive and negative
citations. The sentence vectors formed a feature space, to which the examined
citation sentence was mapped to. Those features were input into classifiers
(support vector machines) for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on a set of annotated
citations. The results showed that word embeddings are effective on classifying
positive and negative citations. However, hand-crafted features performed
better for the overall classification.

本文探讨了利用 word2vec 从 ACL - 嵌入中学习词向量并利用支持向量机进行分类来进行引文情感分析的方法，并发现手工设计的特征在整体分类上表现更好。