Matches in SemOpenAlex for { <https://semopenalex.org/work/W2899347865> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2899347865 abstract "To define salient rhetorical elements in scholarly text, we have earlier defined a set of Discourse Segment Types: semantically defined spans of discourse at the level of a clause with a single rhetorical purpose, such as Hypothesis, Method or Result. In this paper, we use machine learning methods to predict these Discourse Segment Types in a corpus of biomedical research papers. The initial experiment used features related to verb type and form, obtaining F-scores ranging from 0.41–0.65. To improve our results, we explored a variety of methods for balancing classes, before applying classification algorithms. We also performed an ablation study and stepwise approach for feature selection. Through these feature selection processes, we were able to reduce our 37 features to the 9 most informative ones, while maintaining F1 scores in the range of 0.63–0.65. Next, we performed an experiment with a reduced set of target classes. Using only verb tense features, logistic regression, a decision tree classifier and a random forest classifier, we predicted that a segment type was either a Result/Method or a Fact/Implication, with F1 scores above 0.8. Interestingly, findings from this machine learning approach are in line with a reader experiment, which found a correlation between verb tense and a biomedical reader’s interpretation of discourse segment type. This suggests that experimental and concept-centric discourse in biology texts can be distinguished by humans or machines, using verb tense as a key feature." @default.
- W2899347865 created "2018-11-09" @default.
- W2899347865 creator A5024203789 @default.
- W2899347865 creator A5025229650 @default.
- W2899347865 creator A5066817664 @default.
- W2899347865 date "2018-01-01" @default.
- W2899347865 modified "2023-09-23" @default.
- W2899347865 title "Optimized Machine Learning Methods Predict Discourse Segment Type in Biological Research Articles" @default.
- W2899347865 cites W1968466431 @default.
- W2899347865 cites W2040838629 @default.
- W2899347865 cites W2145021085 @default.
- W2899347865 cites W2507306138 @default.
- W2899347865 cites W2509062536 @default.
- W2899347865 cites W2769041395 @default.
- W2899347865 doi "https://doi.org/10.1007/978-3-030-01379-0_7" @default.
- W2899347865 hasPublicationYear "2018" @default.
- W2899347865 type Work @default.
- W2899347865 sameAs 2899347865 @default.
- W2899347865 citedByCount "4" @default.
- W2899347865 countsByYear W28993478652019 @default.
- W2899347865 countsByYear W28993478652021 @default.
- W2899347865 crossrefType "book-chapter" @default.
- W2899347865 hasAuthorship W2899347865A5024203789 @default.
- W2899347865 hasAuthorship W2899347865A5025229650 @default.
- W2899347865 hasAuthorship W2899347865A5066817664 @default.
- W2899347865 hasConcept C119857082 @default.
- W2899347865 hasConcept C138885662 @default.
- W2899347865 hasConcept C148483581 @default.
- W2899347865 hasConcept C154945302 @default.
- W2899347865 hasConcept C169258074 @default.
- W2899347865 hasConcept C192562157 @default.
- W2899347865 hasConcept C204321447 @default.
- W2899347865 hasConcept C2776397901 @default.
- W2899347865 hasConcept C2776401178 @default.
- W2899347865 hasConcept C2780719617 @default.
- W2899347865 hasConcept C41008148 @default.
- W2899347865 hasConcept C41895202 @default.
- W2899347865 hasConcept C84525736 @default.
- W2899347865 hasConcept C95623464 @default.
- W2899347865 hasConceptScore W2899347865C119857082 @default.
- W2899347865 hasConceptScore W2899347865C138885662 @default.
- W2899347865 hasConceptScore W2899347865C148483581 @default.
- W2899347865 hasConceptScore W2899347865C154945302 @default.
- W2899347865 hasConceptScore W2899347865C169258074 @default.
- W2899347865 hasConceptScore W2899347865C192562157 @default.
- W2899347865 hasConceptScore W2899347865C204321447 @default.
- W2899347865 hasConceptScore W2899347865C2776397901 @default.
- W2899347865 hasConceptScore W2899347865C2776401178 @default.
- W2899347865 hasConceptScore W2899347865C2780719617 @default.
- W2899347865 hasConceptScore W2899347865C41008148 @default.
- W2899347865 hasConceptScore W2899347865C41895202 @default.
- W2899347865 hasConceptScore W2899347865C84525736 @default.
- W2899347865 hasConceptScore W2899347865C95623464 @default.
- W2899347865 hasLocation W28993478651 @default.
- W2899347865 hasOpenAccess W2899347865 @default.
- W2899347865 hasPrimaryLocation W28993478651 @default.
- W2899347865 hasRelatedWork W1669912781 @default.
- W2899347865 hasRelatedWork W1894075015 @default.
- W2899347865 hasRelatedWork W1909984404 @default.
- W2899347865 hasRelatedWork W1939171964 @default.
- W2899347865 hasRelatedWork W2057542583 @default.
- W2899347865 hasRelatedWork W2250733885 @default.
- W2899347865 hasRelatedWork W2754603668 @default.
- W2899347865 hasRelatedWork W2763886647 @default.
- W2899347865 hasRelatedWork W2952117893 @default.
- W2899347865 hasRelatedWork W2963299810 @default.
- W2899347865 hasRelatedWork W2963376396 @default.
- W2899347865 hasRelatedWork W2963444785 @default.
- W2899347865 hasRelatedWork W2964175741 @default.
- W2899347865 hasRelatedWork W2970474271 @default.
- W2899347865 hasRelatedWork W3034403033 @default.
- W2899347865 hasRelatedWork W3035027743 @default.
- W2899347865 hasRelatedWork W3114173855 @default.
- W2899347865 hasRelatedWork W3211382138 @default.
- W2899347865 hasRelatedWork W3213181567 @default.
- W2899347865 hasRelatedWork W2825934946 @default.
- W2899347865 isParatext "false" @default.
- W2899347865 isRetracted "false" @default.
- W2899347865 magId "2899347865" @default.
- W2899347865 workType "book-chapter" @default.