From symbolic to sub-symbolic information in question classification

By João Pedro Carlos Gomes da Silva, Luísa Coheur, Ana Cristina Mendes, A. Wichert. 2011

191 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Learned in Translation: Contextualized Word Vectors (2017, neutral): “…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 [Dai and Le, 2015] 92.8 NTI [Munkhdalai and Yu, 2016b] 87.3 bmLSTM [Radford et al.,…”
A Convolutional Neural Network for Modelling Sentences (2014, methodology): “We then relate the results of the experiments and we inspect the learnt feature detectors.”
Convolutional Neural Networks for Sentence Classification (2014, methodology): “SVM S : SVM with uni-bi-trigrams, wh word, head word, POS, parser, hypernyms, and 60 hand-coded rules as features from Silva et al. (2011). to both channels, but gradients are back-propagated only through one of the channels.”
Discriminative Neural Sentence Modeling by Tree-Based Convolution (2015, methodology): “, 2010), or more dedicated ones (Silva et al., 2011)—can play an important role in modeling sentences.”
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks (2016, methodology): “Many different approaches have been developed for short-text classiﬁcation, such as using Support Vector Machines (SVMs) with rule-based features (Silva et al., 2011), combining SVMs with naive Bayes (Wang and Manning, 2012), and building dependency trees with Conditional Random Fields (Nakagawa et…”
Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features (2011, methodology): “Similar to [6,13], we extracted the head word based on the syntactical structure of the question.”
The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization (2015, methodology): “…et al., 2014), MaxEntH (Huang et al., 2008) (which uses MaxEnt with uni-bi-trigrams, POS, wh-word, head word, word shape, parser, hypernyms, WordNet) and the SVMS (Silva et al., 2011) (which uses SVM with, in addition to features used by MaxEntH , 60 hand-coded rules).”
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection (2016, methodology): “[28] chose sentence unigrams as input feature vector, and trained the SVM model.”
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%.”
Semantic Clustering and Convolutional Neural Network for Short Text Categorization (2015, methodology): “6 DCNN – 93 (Kalchbrenner et al,2014) SVMS – 95 (Silva et al., 2011) CNN-TwoChannel – 93.”
Dependency-based Convolutional Neural Networks for Sentence Embedding (2015, background): “Like ours, Silva et al. (2011) is a tree-based system but it uses constituency trees compared to ours dependency trees.”
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (2018, methodology): “SVM, CNN and RNN models (Silva et al., 2011; Hashemi et al., 2016; Shi et al., 2016) perform well for intent classification.”
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents (2016, methodology): “ning, 2012) SVMS : SVM with features including uni-bi-trigrams, POS, parser, and 60 hand-coded rules (Silva et al., 2011) Standard-RNN: Standard Recursive Neural Network (Socher et al.”