Learning question classifiers: the role of semantic information

By Xin Li, Dan Roth. 2005

326 citations recorded by the source corpus.

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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…”
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks (2016, methodology): “We implement binary sentiment classification using the Stanford Sentiment Treebank dataset, and the coarse-grained question classification task described in Li and Roth (2006).”
Emotions from Text: Machine Learning for Text-based Emotion Prediction (2005, methodology): “tial model that further defines levels of coarse versus fine-grained classifiers, as done by (Li and Roth, 2002) for thequestion classificationproblem.”
Auditing Deep Learning processes through Kernel-based Explanatory Models (2019, methodology): “We replicated the experiments reported in (Croce et al., 2017) with respect to the question classiﬁ-cation task, using the UIUC dataset (Li and Roth, 2006), including a training and test set of 5452 and 500 questions, respectively, organized in 6 coarse-grained classes (as ENTITY or HUMAN ).”
Question Classification using Head Words and their Hypernyms (2008, background): “Considering also examples (Li and Roth, 2006) What tourist attractions are there in Reims , What are the names of the tourist attractions in Reims , What do most tourists visit in Reims , What attracts tourists to Reims , and What is worth seeing in Reims , all these reformulations are of the same…”
What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams (2016, methodology): “Factoid QA systems use classifiers to identify the types of question and expected answers, which are subsequently used to select specific problem solving routines, and to filter answer candidates (Harabagiu et al., 2000; Li and Roth, 2006; Roberts and Hickl, 2008).”
Bootstrapping Dialog Systems with Word Embeddings (2014, methodology): “We also evaluate the methods on a question classification dataset [9] with 15,452 short questions labelled into 50 classes (person, country, money, etc.”
Interactive Feature Space Construction using Semantic Information (2009, background): “have been shown to induce more accurate classifiers (Boggess et al., 1991; Miller et al., 2004; Li and Roth, 2005), naive approaches may instead result in higher sample complexity due to increased ambiguity introduced through these semantic resources.”
Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features (2011, methodology): “Li and Roth [9] obtained an accuracy of 84.0% on the fine grained classes using a SNoW (Sparse Network of Winnows) architecture.”
Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees (2006, methodology): “We consider the coarse grained classification scheme described in [16, 17]: Abbreviations, Descriptions (e.”
Syntactic and Semantic Kernels for Short Text Pair Categorization (2009, methodology): “The datasets are based on the 138 TREC 2001 test questions labeled as “description” in (Li and Roth, 2005).”
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (2019, methodology): “…tasks such as relation extraction (Gamallo et al., 2012), semantic role labeling (Sutton and McCallum, 2005; He et al., 2018), machine translation (Aharoni and Goldberg, 2017; Eriguchi et al., 2017; Zaremoodi and Haffari, 2018), and text classification (Li and Roth, 2006; Tai et al., 2015).”