A MACHINE LEARNING APPROACH FOR CORRECTING THE ERRORS OF A TREEBANK

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ZAREI FARZANEH; FAILI HESHAAM; MIRIAN MARYAM

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Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2015 | Volume:- | Issue:3 (SERIAL 25) Page(s): 99-108

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Information Journal Paper

Title

A MACHINE LEARNING APPROACH FOR CORRECTING THE ERRORS OF A TREEBANK

Author(s)

ZAREI FARZANEH | FAILI HESHAAM | MIRIAN MARYAM | Issue Writer Certificate

Keywords

TREEBANKQ2

ERROR DETECTION AND CORRECTIONQ2

TREE ADJOINING GRAMMARQ2

TRANSFORMATION BASED LEARNINGQ2

Abstract

The TREEBANK is one of the most useful resources for supervised or semi-supervised learning in many NLP tasks such as speech recognition, spoken language systems, parsing and machine translation. TREEBANK can be developded in different ways that could be, generally, categorized in manually and statistical approaches.While the resulted TREEBANK in each of these methods has the annotation error, one which accomplished by statistical method has much more errors than the other. Error in Treenabanks causes that they are not useful anymore. In this paper an statistical method is proposed which aims to correct the errors in a specific English LTAG-TREEBANK. The proposed method was applied to a automatically generated TREEBANK and an improvement from 68% to 79% respect to F-measure is retrieved.

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APA: Copy

ZAREI, FARZANEH, FAILI, HESHAAM, & MIRIAN, MARYAM. (2015). A MACHINE LEARNING APPROACH FOR CORRECTING THE ERRORS OF A TREEBANK. SIGNAL AND DATA PROCESSING, -(3 (SERIAL 25)), 99-108. SID. https://sid.ir/paper/160869/en

Vancouver: Copy

ZAREI FARZANEH, FAILI HESHAAM, MIRIAN MARYAM. A MACHINE LEARNING APPROACH FOR CORRECTING THE ERRORS OF A TREEBANK. SIGNAL AND DATA PROCESSING[Internet]. 2015;-(3 (SERIAL 25)):99-108. Available from: https://sid.ir/paper/160869/en

IEEE: Copy

FARZANEH ZAREI, HESHAAM FAILI, and MARYAM MIRIAN, “A MACHINE LEARNING APPROACH FOR CORRECTING THE ERRORS OF A TREEBANK,” SIGNAL AND DATA PROCESSING, vol. -, no. 3 (SERIAL 25), pp. 99–108, 2015, [Online]. Available: https://sid.ir/paper/160869/en

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