Journal ArticleDOI
Feature extraction based on information gain and sequential pattern for English question classification
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TLDR
This study presents a hybrid approach to semantic feature extraction and lexical feature extraction, which achieves a coarse accuracy of 96% and fine accuracy of 90.4%, which is superior to existing methods.Abstract:
The purpose of question classification (QC) is to assign a question to an appropriate category from the set of predefined categories that constitute a question taxonomy. Selected question features are able to significantly improve the performance of QC. However, feature extraction, particularly syntax feature extraction, has a high computational cost. To maintain or enhance performance without syntax features, this study presents a hybrid approach to semantic feature extraction and lexical feature extraction. These features are generated by improved information gain and sequential pattern mining methods, respectively. Selected features are then fed into classifiers for questions classification. Benchmark testing is performed using the public UIUC data set. The results reveal that the proposed approach achieves a coarse accuracy of 96% and fine accuracy of 90.4%, which is superior to existing methods.read more
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References
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Proceedings ArticleDOI
Verb semantics and lexical selection
Zhibiao Wu,Martha Palmer +1 more
Abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
Proceedings ArticleDOI
Learning question classifiers
TL;DR: A hierarchical classifier is learned that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes.
Proceedings ArticleDOI
Question classification using support vector machines
Dell Zhang,Wee Sun Lee +1 more
TL;DR: This paper proposes to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions, and describes how the tree Kernel can be computed efficiently by dynamic programming.
Journal ArticleDOI
Scaling question answering to the web
TL;DR: Mulder is introduced, which is believed to be the first general-purpose, fully-automated question-answering system available on the web, and its architecture is described, which relies on multiple search-engine queries, natural-language parsing, and a novel voting procedure to yield reliable answers coupled with high recall.
Journal ArticleDOI
Learning question classifiers: the role of semantic information
TL;DR: The authors developed a hierarchical classifier that classifies questions into fine-grained classes, guided by a layered semantic hierarchy of answer types, and performed a systematic study of the use of semantic information sources in natural language classification tasks.
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