Abstract
Objective: The paper explores the influence of sample size, the N value of N-gram, stop words, and weighting methods of word frequency on the automatic recognition of rhetorical moves in scientific paper, aiming to improve the abstracting method based on support vector machine (SVM) model.
Methods: We retrieved a total of 1.1 million labeled moves from 720, 000 structured abstracts of scientific papers as experimental data, and constructed SVM model for move recognition. Based on the principle of single variable, we used control variable method by changing the sample size, the N value, removal of stop words, and word frequency weighting methods to analyze their impacts on the model's performance.
Results: We found that the model yielded the best result with a sample size of 600, 000 abstracts, the N value [1, 2], keeping stop words, and using TF-IDF to weight word frequency.
Limitations: We only examined the model with structured abstracts, which might not be comparable with other studies.
Conclusions: The sample size and some fine features have significant impacts on the performance of traditional machine learning models.
| Translated title of the contribution | Factors Affecting Rhetorical Move Recognition with SVM Model |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 16-23 |
| Number of pages | 8 |
| Journal | Data Analysis and Knowledge Discovery |
| Volume | 3 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2019 |
Keywords
- move Recognition
- structured abstracts
- support vector machine
Research Beacons, Institutes and Platforms
- Manchester Institute of Innovation Research