Using learner corpora for automatic error detection and correction

2013 ◽  
pp. 127-150 ◽  
Author(s):  
Michael Gamon ◽  
Martin Chodorow ◽  
Claudia Leacock ◽  
Joel Tetreault
Keyword(s):  
Error Detection ◽  
Learner Corpora ◽  
Error Detection And Correction ◽  
Automatic Error

scholarly journals A Probabilistic Approach to Error Detection&Correction for Tree-Mapping Grammars

2018 ◽  
Vol 111 (1) ◽  
pp. 97-112
Author(s):  
Tim vor der Brück
Keyword(s):  
Error Detection ◽  
Basic Assumption ◽  
Probabilistic Approach ◽  
Large Set ◽  
Generation Process ◽  
Input Structure ◽  
Error Detection And Correction ◽  
Complete Failure ◽  
Automatic Error

Abstract Rule-based natural language generation denotes the process of converting a semantic input structure into a surface representation by means of a grammar. In the following, we assume that this grammar is handcrafted and not automatically created for instance by a deep neural network. Such a grammar might comprise of a large set of rules. A single error in these rules can already have a large impact on the quality of the generated sentences, potentially causing even a complete failure of the entire generation process. Searching for errors in these rules can be quite tedious and time-consuming due to potentially complex and recursive dependencies. This work proposes a statistical approach to recognizing errors and providing suggestions for correcting certain kinds of errors by cross-checking the grammar with the semantic input structure. The basic assumption is the correctness of the latter, which is usually a valid hypothesis due to the fact that these input structures are often automatically created. Our evaluation reveals that in many cases an automatic error detection and correction is indeed possible.

ERROR DETECTION AND CORRECTION BASED ON CHINESE PHONEMIC ALPHABET IN CHINESE TEXT

2008 ◽  
Vol 16 (supp01) ◽  
pp. 89-105 ◽  
Author(s):  
CHUEN-MIN HUANG ◽  
MEI-CHEN WU ◽  
CHING-CHE CHANG
Keyword(s):  
Chinese Text ◽  
Error Detection ◽  
Experimental Results ◽  
Text Editor ◽  
Correction Rate ◽  
Error Detection And Correction ◽  
Chinese Texts ◽  
Chinese Writing ◽  
Automatic Error

Misspelling and misconception resulting from similar pronunciation appears frequently in Chinese texts. Without double check-up, this situation will be getting worse even with the help of Chinese input editor. It is hoped that the quality of Chinese writing would be enhanced if an effective automatic error detection and correction mechanism is embedded in text editor. Therefore, the burden of manpower to proofread shall be released. Until recently, researches in automatic error detection and correction of Chinese text have undergone many challenges and suffered from bad performance compared with that of Western text. In view of the prominent phenomenon in Chinese writing problem, this study proposes a learning model based on Chinese phonemic alphabets. The experimental results demonstrate that this model is effective in finding out misspellings and further improves detection and correction rate.

Background 3

2009 ◽  
pp. 110-144
Author(s):  
Piotr Augustyniak ◽  
Ryszard Tadeusiewicz
Keyword(s):  
Error Detection ◽  
Medical Information ◽  
General Purpose ◽  
Digital Data ◽  
Human Errors ◽  
Error Detection And Correction ◽  
Database Operations ◽  
Information Interchange ◽  
Automatic Error

This chapter defines the set of standard diagnostic parameters and metadata expected from cardiac examinations. Rest ECG, exercise ECG, and long-term recording techniques are compared with regard to method-appropriate hierarchies of diagnostic results. This summary is approaching the idea of high redundancy in the dataset influencing data transmission and database operations. As far as the paper record was concerned, these spare data were useful in the validation and correction of human errors. Nowadays, automatic error detection and correction codes are widely applied in systems for storage and transmission of digital data. Basic issues about DICOM and HL7, two widespread medical information interchange systems, are presented thereafter. These general-purpose systems integrate multi-modal medical data and offer specialized tools for the storage, retrieval, and management of data. Both standards originate from the efforts of standardizing the description of possibly wide aspects of patient-oriented digital data in the form of electronic health records. Certain aspects of data security are also considered here.

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