Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features
By Babak Loni, Gijs van Tulder, Pascal Wiggers, David Martinus Johannes Tax, Marco Loog. 2011
36 citations recorded by the source corpus.
Subfields: Artificial Intelligence
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In Artificial Intelligence
Citations in the corpus
- Learned in Translation: Contextualized Word Vectors (2017, neutral): “MVN [Guo et al., 2017] 51.5 T R E C - 50 SVM [Loni et al., 2011] 89.0 DMN [Kumar et al., 2016] 52.1 SNoW [Li and Roth, 2006] 89.3 LSTM-CNN [Zhou et al., 2016] 52.4 BCN+Char+CoVe (Ours) 90.2 TE-LSTM [Huang et al., 2017] 52.6 RulesUHC [da Silva et al., 2011] 90.8 NTI [Munkhdalai and Yu, 2016b] 53…”
- High Accuracy Rule-based Question Classification using Question Syntax and Semantics (2016, methodology): “Of specific relevance to our work is the work by Silva et al. (2011), who first extract headwords, before then mapping these headwords into various categories using WordNet (Miller, 1995) to achieve an accuracy of 90.8%.”