From symbolic to sub-symbolic information in question classification

Abstract

Question Answering (QA) is undoubtedly a growing field of current research in Artificial Intelligence. Question classification, a QA subtask, aims to associate a category to each question, typically representing the semantic class of its answer. This step is of major importance in the QA process, since it is the basis of several key decisions. For instance, classification helps reducing the number of possible answer candidates, as only answers matching the question category should be taken into account. This paper presents and evaluates a rulebased question classifier that partially founds its performance in the detection of the question headword and in its mapping into the target category through the use of WordNet. Moreover, we use the rule-based classifier as a features' provider of a machine learning-based question classifier. A detailed analysis of the rule-base contribution is presented. Despite using a very compact feature space, state of the art results are obtained.

Ambiguity in Question Paper Translation

“…In 2011, Silva et al [31] worked on a question answering system by using question classification from symbolic to sub-symbolic information. Authors also gave the information about last few year work done on supervised machine learning approaches to question classification.…”

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

“…In Ma et al (2015), convolutions are guided by dependencies linking question words, but it is not clear how the word vectors are initialized. In our case, we only use pre-trained word vectors and the output of a parser, avoiding intensive manual feature engineering, as in Silva et al (2010). The accuracy of these models are reported in Tab.…”

“…They can effec- Table 1: QC accuracy (%) and description of SVM (Silva et al, 2010), DCNN (Kalchbrenner et al, 2014), CNNns (Kim, 2014), DepCNN, (Ma et al, 2015) and SPTK (Croce et al, 2011) models.…”

Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information

“…They can effec- Table 1: QC accuracy (%) and description of SVM (Silva et al, 2010), DCNN (Kalchbrenner et al, 2014), CNNns (Kim, 2014), DepCNN, (Ma et al, 2015) and SPTK (Croce et al, 2011) models. tively encode lexical, syntactic and semantic information in learning algorithms. For this purpose, they count the number of substructures shared by two trees.…”