Subcategorisation and the result adverbials were the two worst performing features. Van Dijk, T. A. (2016). 77–80. The breakdown of how many types of modal senses which occurred is displayed in Table 6.
That is the reason why we can afford to only look for shorter problem and solution descriptions, ignoring those that cross sentence boundaries. “limit”,“explosion”).
In LREC. Syntax Next, a set of syntactic features were defined by using the presence of POS tags in each candidate.
If the string expressed a research task, without explicit statement that there was anything problematic about it (i.e., the ‘wrong’ sense of “problem”, as described above), it was allowed as a non-problem. Logistic Regression performed best overall and so this model was chosen for our upcoming experiments. There are exceptions to this, as authors will sometimes introduce a problem and then leave it to future work, but overall there should be enough signal in the Problem element to make our method of looking for it in isolation worthwhile. https://www.uni-hildesheim.de/ruppenhofer/data/modalia_release1.0.tgz.
Rosemarie Chang, psychology instructor at the State University of New York at New Paltz, asked study subjects to do math problems while listening to a variety of annoying background noises.
Briscoe, T., Carroll, J., & Watson, R. (2006). Again.”. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. Lastly, when creating our polarity feature we query SentiWordNet using synsets assigned by the Lesk algorithm. Siegel, S. (1956).
(Mohit et al. Theoretically, we know that sentiment detection is related to problem-solving activity, because of the perception that “bad” situations are transformed into “better” ones via problem-solving.
Research as pragmatic problem-solving: The pragmatist roots of empirically-grounded theorizing. Knowledge of syntax provides a big improvement with a significant increase over the baseline results from two of the classifiers. For solutions, the phrase had to represent a response to a problem with a positive evaluation. However, we also know that short summaries of problems and solutions are very prevalent in science, and also that these tend to occur in the most prominent places in a paper.
Zhou, M., Frank, A., Friedrich, A., & Palmer, A. This subset of features managed to improve results in both the Naïve Bayes and SVM classifiers with the highest overall result coming from the SVM (82.3%). This new model was then used in the upcoming experiment to predict modality labels for each instance in our dataset.
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