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A customised grammar framework for query classification

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A customised grammar framework for query classification. / Mohasseb, Alaa; Bader-El-Den, Mohamed; Cocea, Mihaela.

In: Expert Systems with Applications, Vol. 135, 30.11.2019, p. 164-180.

Research output: Contribution to journalArticlepeer-review

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@article{7b75ba8968974d30a2219649b329e03d,
title = "A customised grammar framework for query classification",
abstract = "In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts, but have a different meaning. A promising avenue for text classification is machine learning, which has been shown to perform well in a variety of applications including query classification and sentiment analysis. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this paper, we propose a Customised Grammar Framework for text classification, which exploits domain-related information and a new way to represent text as a series of syntactic categories forming syntactic patterns. The framework employs a formal grammar approach for transforming the text into the syntactic patterns representation. We applied the framework for the query classification problem and our results show that our approach outperforms previous ones in terms of classification performance.",
keywords = "Natural Language Processing, Information Retrieval, Text Classification, Query Classification, MachineLearning",
author = "Alaa Mohasseb and Mohamed Bader-El-Den and Mihaela Cocea",
year = "2019",
month = nov,
day = "30",
doi = "10.1016/j.eswa.2019.06.010",
language = "English",
volume = "135",
pages = "164--180",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - A customised grammar framework for query classification

AU - Mohasseb, Alaa

AU - Bader-El-Den, Mohamed

AU - Cocea, Mihaela

PY - 2019/11/30

Y1 - 2019/11/30

N2 - In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts, but have a different meaning. A promising avenue for text classification is machine learning, which has been shown to perform well in a variety of applications including query classification and sentiment analysis. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this paper, we propose a Customised Grammar Framework for text classification, which exploits domain-related information and a new way to represent text as a series of syntactic categories forming syntactic patterns. The framework employs a formal grammar approach for transforming the text into the syntactic patterns representation. We applied the framework for the query classification problem and our results show that our approach outperforms previous ones in terms of classification performance.

AB - In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts, but have a different meaning. A promising avenue for text classification is machine learning, which has been shown to perform well in a variety of applications including query classification and sentiment analysis. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this paper, we propose a Customised Grammar Framework for text classification, which exploits domain-related information and a new way to represent text as a series of syntactic categories forming syntactic patterns. The framework employs a formal grammar approach for transforming the text into the syntactic patterns representation. We applied the framework for the query classification problem and our results show that our approach outperforms previous ones in terms of classification performance.

KW - Natural Language Processing

KW - Information Retrieval

KW - Text Classification

KW - Query Classification

KW - MachineLearning

U2 - 10.1016/j.eswa.2019.06.010

DO - 10.1016/j.eswa.2019.06.010

M3 - Article

VL - 135

SP - 164

EP - 180

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -

ID: 14385360