Learning to Identify Ambiguous and Misleading News Headlines
Authors: Wei Wei, Xiaojun Wan
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis. |
| Researcher Affiliation | Academia | Wei Wei and Xiaojun Wan Institute of Computer Science and Technology, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University {weiwei718,wanxiaojun}@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 Co-Training Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. It only links to a third-party library used. |
| Open Datasets | No | The paper states: 'we randomly select 2924 pieces of news and employ 6 college students majoring in Chinese to label each news headline as ambiguous or not, and also label it as misleading or not.' It does not provide concrete access information for this custom dataset. |
| Dataset Splits | Yes | We randomly split the labeled data set into a training set and a test set in the proportion of 3:1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools such as 'SVM toolkit in scikit-learn', 'LTP parser', 'PKUSUMSUM', and vocabularies from 'Hownet' and 'Sogou Input', but it does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | When mining class sequential rules, we conduct experiments with different minimum support and minimum confidence. In Table 3, we list the result of minsup 0.02 and minconf 0.8 since this set of parameters fit well on the training set. We use the SVM classifier and the rbf kernel. We use different sets of parameters and the experiment results listed in Table 5 is applied to the parameter set of p = 10, n = 20 and iteration number = 50. |