Natural Language Processing (NLP) tools perform best if they are used on the same kind of content on which they were trained and tested. Unfortunately for those in the STM domains, our content has some big differences from the newswire text that is commonly used in the development of most NLP tools. There are some corpora of STM content, but the ones we know of are specific to one domain, such as biomedicine, and will typically consist of abstracts instead of full articles. This is less than optimal because math articles are very different from biomed articles, and articles are very different from abstracts.
To improve this situation, Elsevier is providing a selection of articles from 10 different STM domains as a freely-redistributable corpus. The articles were selected from our Open Access content and have a Creative Commons CC-BY license so they are free to redistribute and use. The domains are agriculture, astronomy, biology, chemistry, computer science, earth science, engineering, materials science, math, and medicine. Currently we provide 11 articles in each of the 10 domains. (We also provide instructions on how to find all of our Open Access CC-BY content.)
For each article in the corpus we provide:
- the XML source,
- a simple text version for easier text mining,
- several versions with different annotations. These include part of speech tags, sentence breaks, NP and VP chunks, lemmas, syntactic constituents parses, wikipedia concept identification, and discourse analysis.
Most of the annotations are automatically created. However, we have identified 10 documents as a default test set. As new annotation types are added, those articles should be the first choice for manually reviewed and corrected test data.
To seed the process of creating manual annotations, Elsevier has commissioned a treebank of the ten full-text articles in the default test set. We hope this corpus and treebank become a valuable resource for NLP, linguistics, and text mining researchers, developers, and users.
In addition to having a wide-ranging STM corpus, we also hope that the content becomes densely annotated with many different types of annotations. Not only would that allow comparison of algorithms for the same type of annotations, it would also allow for the automatic selection of features to be used in creating higher-order annotations.
The corpus currently consists of:
| Directory | Description | Count |
|---|---|---|
| SourceXML | Slightly modfied XML source for published article. | 110 |
| SourceText | Readable text extract for easy text mining. | 110 |
| CoreSC | Annotations for CoreSC Discourse model. | 110 |
| Treebank | Manually created treebank for 10 articles. | 10 |
| Stanford | Automatic annotations from Standord Core NLP. | 110 |
| GENIA | Automatic annotions from GENIA tools. | 110 |
| Wikification | Annotations from Spotlight and similar tools. | 10 |
Elsevier currently has about 12 million articles in a wide range of disciplines. About 600,000 of those are free to read articles. The published PDF version of those articles can be freely downloaded from ScienceDirect for reading. Of those articles, about 15,000 have a CC-BY license which lets you redistribute the PDF, make commercial use of the content, and prepare derivative works. Just don't claim the papers as your work unless you are one of the authors. The corpus is 110 articles selected from those 15,000. They come from 10 domains that are most-published. For the corpus we provide the XML source for the article, as well as various other versions of the text such as the SimpleText format and the various annotated versions. The default test set is 10 articles from the 110, one per domain.