| Title |
Identifying problems and solutions in scientific text
|
|---|---|
| Published in |
Scientometrics, April 2018
|
| DOI | 10.1007/s11192-018-2718-6 |
| Pubmed ID | |
| Authors |
Kevin Heffernan, Simone Teufel |
| Abstract |
Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 81 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 11% |
| Researcher | 7 | 9% |
| Student > Ph. D. Student | 6 | 7% |
| Student > Bachelor | 6 | 7% |
| Librarian | 4 | 5% |
| Other | 16 | 20% |
| Unknown | 33 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 22% |
| Social Sciences | 9 | 11% |
| Business, Management and Accounting | 3 | 4% |
| Linguistics | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 2% |
| Other | 9 | 11% |
| Unknown | 37 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 April 2018.
All research outputs
#14,064,812
of 23,314,015 outputs
Outputs from Scientometrics
#1,808
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Outputs of similar age
#177,621
of 330,268 outputs
Outputs of similar age from Scientometrics
#39
of 60 outputs
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