CUFE$@$QALB-2015 Shared Task: Arabic Error Correction System
نویسنده
چکیده
In this paper we describe the implementation of an Arabic error correction system developed for the WANLP-2015 shared task on automatic error correction for Arabic text. We proposed improvements to a previous statistical rule based system, where we use the words patterns to improve the error correction, also we have used a statistical system the syntactic error correction rules. The system achieves an F-score of 0.7287 on the Aljtest-2015 dataset, and an F-score of 0.3569 on the L2-test-2015 dataset.
منابع مشابه
QCRI$@$QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers' Errors
This paper describes the error correction model that we used for the QALB2015 Automatic Correction of Arabic Text shared task. We employed a case-specific correction approach that handles specific error types such as dialectal word substitution and word splits and merges with the aid of a language model. We also applied corrections that are specific to second language learners that handle erron...
متن کاملUMMU$@$QALB-2015 Shared Task: Character and Word level SMT pipeline for Automatic Error Correction of Arabic Text
In this paper we present the LIUM (Laboratoire d’Informatique de l’Universit du Maine) and CMU-Q (Carnegie Mellon University in Qatar) joint submission in the Arabic shared task on automatic spelling error correction. Our best system is a sequential combination of two statistical machine translation systems (SMT) trained on top of the MADAMIRA output. The first is a Character-based one, used to...
متن کاملQCMUQ$@$QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction
We describe the CMU-Q and QCRI’s joint efforts in building a spelling correction system for Arabic in the QALB 2015 Shared Task. Our system is based on a hybrid pipeline that combines rule-based linguistic techniques with statistical methods using language modeling and machine translation, as well as an error-tolerant finite-state automata method. We trained and tested our spelling corrector us...
متن کاملThe Second QALB Shared Task on Automatic Text Correction for Arabic
We present a summary of QALB-2015, the second shared task on automatic text correction of Arabic texts. The shared task extends QALB-2014, which focused on correcting errors in Arabic texts produced by native speakers of Arabic. The competition this year, in addition to native data, includes texts produced by learners of Arabic as a foreign language. The report includes an overview of the QALB ...
متن کاملCMUQ$@$QALB-2014: An SMT-based System for Automatic Arabic Error Correction
In this paper, we describe the CMUQ system we submitted to The ANLP-QALB 2014 Shared Task on Automatic Text Correction for Arabic. Our system combines rule-based linguistic techniques with statistical language modeling techniques and machine translationbased methods. Our system outperforms the baseline and reaches an F-score of 65.42% on the test set of QALB corpus. This ranks us 3rd in the com...
متن کامل