# A Sequential Graph Neural Network for Short Text Classification
## 3 Materials and Experiments
### Datasets  
We conducted experiments on seven short text datasets, including R8, R52, MR, SearchSnippets, SMS, and Biomedical. The detailed description of each dataset is listed below.  
* R52 and R8 are two subsets of the Reuters 21,578 dataset ([[http://disi.unitn.it/moschitti/corpora.htm](http://disi.unitn.it/moschitti/corpora.htm)]([http://disi.unitn.it/moschitti/corpora.htm](http://disi.unitn.it/moschitti/corpora.htm))) (accessed on 30 November 2021). R8 has 8 categories, which were split to 5485 training and 2189 test documents. R52 has 52 categories, which were split to 6532 training and 2568 test documents.
* Ag News ([[http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html)]([http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html))) (accessed on 30 November 2021) is a news dataset from [48], which consists of the following four topics: World, Sports, Business and Sci/Tech. We randomly selected 4000 items from each category to form a dataset for the experiment. In our experiment we called it Ag News Sub.
* MR ([[https://github.com/mnqu/PTE/tree/master/data/mr](https://github.com/mnqu/PTE/tree/master/data/mr)]([https://github.com/mnqu/PTE/tree/master/data/mr](https://github.com/mnqu/PTE/tree/master/data/mr))) (accessed on 30 November 2021) is a movie review dataset for binary sentiment classification, where each review contains only one sentence [49]. The corpus has 5331 positive and 5331 negative reviews. We used the same training and test set division methods as [50].
* SearchSnippets ([[http://jwebpro.sourceforge.net/data-web-snippets.tar.gz](http://jwebpro.sourceforge.net/data-web-snippets.tar.gz)]([http://jwebpro.sourceforge.net/data-web-snippets.tar.gz](http://jwebpro.sourceforge.net/data-web-snippets.tar.gz))) (accessed on 30 November 2021) dataset is released by [51], which contains 12,340 documents. It is composed of the search results which based on 8 different domains terms in search engines, including business, computer, health, education, etc.
* SMS ([[http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/)]([http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/))) (accessed on 30 November 2021) is a binary classification dataset collected for short message spam research, which contains 5574 pieces of English, real and unencrypted messages.
* Biomedical is built by [52] from an internationally renowned biomedical platform BioASQ ([[http://participants-area.bioasq.org/](http://participants-area.bioasq.org/)]([http://participants-area.bioasq.org/](http://participants-area.bioasq.org/))) (accessed on 30 November 2021) and contains 20,000 documents. It consists of 20 categories of titles of the papers that belong to the MeSH theme as the dataset.  
We first preprocessed all the datasets by cleaning and tokenizing text as [28]. Then, we deleted stop words defined in NLTK and low-frequency words that appeared less than 5 times for R8, R52, and Ag news sub. For the other four datasets, we did not delete words after cleaning and tokenizing raw text because the documents were very short, so word sequence S1 and word sequence S2 were the same. The statistics of the datasets are shown in Table 2.