With the increasing abundance of literature resources, how to acquire knowledge elements efficiently and accurately is the key to achieving accurate literature retrieval and utilization of available literature resources. The identification of the structure function of academic documents is a fundamental work to meet the above requirements. In this study, the proceedings of the Association for Computational Linguistics (ACL) conferences are used as the primitive corpus, and the training corpus of chapter category is obtained by manual annotation. Based on the chapter titles and the in-chapter texts, traditional machine learning and deep learning models are both used for classifier training. Our results show that the title of a chapter is more beneficial to the identification of the structure function of academic documents than the in-chapter texts. The highest F1 value in our experiments is 0.9249, which is obtained on the traditional logistic regression (LR) and support vector machine (SVM) models (slightly higher than on the convolutional neural network [CNN]). And through the experiment of adding other chapter characteristics based on the traditional model, we find that combining the relative position of chapters can effectively improve the classification performance. Finally, this study compares the results of experimental groups with different methods, analyzes the misclassification of the structure function of academic documents, and points out the main direction to improve the classification performance in the future.
Traditional set-aside theory is subject to considerable challenges as a result of an uncompromising trend towards autonomy and internationalism in international arbitration. The silence and ambiguity of international law regarding enforcement of set-aside arbitral awards allow some states to abandon their own set-aside authority or ignore set-aside decisions made by competent courts. This article presents a range of evidence that demonstrates the enforcement of set-aside arbitral awards has become a common phenomenon. This article first introduces robust academic debates regarding set-aside authority. Then this article exposes omission and ambiguity in the legal source, which leads to confusion in enforcement proceedings of set-aside arbitral awards. This article describes and analyses selected cases and practical data in order to summarize the approaches taken by national courts when reviewing foreign set-aside decisions. Finally, this article briefly evaluates the most promising solutions to the contradictory enforcement proceedings of set-aside arbitral awards.
This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles.
Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys.
In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-WordNet and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey quality.
The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing.
CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain field.
CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.