We got some great feedback from reviewers our new Sloan grant, including a suggestion that we be more transparent about our process over the course of the grant. We love that idea, and you’re now reading part of our plan for how to do that: we’re going to be blogging a lot more about what we learn as we go.
A big part of the grant is using machine learning to automatically discover mentions of software use in the research literature. It’s going to be a really fun project because we’ll get to play around with some of the very latest in ML, which currently The Hotness everywhere you look. And we’re learning a lot as we go. One of the first questions we’ve tackled (also in response to some good reviewer feedback) is: how big does our training set need to be? The machine learning system needs to be trained to recognized software mentions, and to do that we need to give it a set of annotated papers where we, as humans, have marked what a software mention looks like (and doesn’t look like). That training set is called the gold standard. It’s what the machine learning system learns from. Below is copied from one of our reviewer responses:
We came up with the number of articles to annotate through a combination of theory, experience, and intuition. As usual in machine learning tasks, we considered the following aspects of the task at hand:
- prevalence: the number of software mentions we expect in each article
- task complexity: how much do software-mention words look like other words we don’t want to detect
- number of features: how many different clues will we give our algorithm to help it decide whether each word is a software mention (eg is it a noun, is it in the Acknowledgements section, is it a mix of uppercase and lowercase, etc)
None of these aspects are clearly understood for this task at this point (one outcome of the proposed project is that we will understand them better once we are done, for future work), but we do have rough estimates. Software mention prevalence will be different in each domain, but we expect roughly 3 mentions per paper, very roughly, based on previous work by Howison et al. and others. Our estimate is that the task is moderately complex, based on the moderate f-measures achieved by Pan et al. and Duck et al. with hand-crafted rules. Finally, we are planning to give our machine learning algorithm about 100 features (50 automatically discovered/generated by word2vec, plus 50 standard and rule-based features, as we discuss in the full proposal).
We then used these estimates. As is common in machine learning sample size estimation, we started by applying a rule-of-thumb for the number of articles we’d have to annotate if we were to use the most simple algorithm, a multiple linear regression. A standard rule of thumb (see https://en.wikiversity.org/wiki/Multiple_linear_regression#Sample_size) is 10-20 datapoints are needed for each feature used by the algorithm, which implies we’d need 100 features * 10 datapoints = 1000 datapoints. At 3 datapoints (software mentions) per article, this rule of thumb suggests we’d need 333 articles per domain.
From there we modified our estimate based on our specific machine learning circumstance. Conditional Random Fields (our intended algorithm) is a more complex algorithm than multiple linear regression, which might suggest we’d need more than 333 articles. On the other hand, our algorithm will also use “negative” datapoints inherent in the article (all the words in the article that are *not* software mentions, annotated implicitly as not software mentions) to help learn information about what is predictive of being vs not being a software mention — the inclusion of this kind of data for this task means our estimate of 333 articles is probably conservative and safe.
Based on this, as well as reviewing the literature for others who have done similar work (Pan et al. used a gold standard of 386 papers to learn their rules, Duck et al. used 1479 database and software mentions to train their rule weighting, etc), we determined that 300-500 articles per domain was appropriate. We also plan to experiment with combining the domains into one general model — in this approach, the domain would be added as an additional feature, which may prove more powerful overall. This would bring all 1000-1500 articles to the test set.
Finally, before proposing 300-500 articles per domain, we did a gut-check whether the proposed annotation burden was a reasonable amount of work and cost for the value of the task, and we felt it was.
Duck, G., Nenadic, G., Filannino, M., Brass, A., Robertson, D. L., & Stevens, R. (2016). A Survey of Bioinformatics Database and Software Usage through Mining the Literature. PLOS ONE, 11(6), e0157989. http://doi.org/10.1371/journal.pone.0157989
Howison, J., & Bullard, J. (2015). Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature. Journal of the Association for Information Science and Technology (JASIST), Article first published online: 13 MAY 2015. http://doi.org/10.1002/asi.23538
Pan, X., Yan, E., Wang, Q., & Hua, W. (2015). Assessing the impact of software on science: A bootstrapped learning of software entities in full-text papers. Journal of Informetrics, 9(4), 860–871. http://doi.org/10.1016/j.joi.2015.07.012
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