Informing Determiner and Preposition Error Correction with Hierarchical Word Clustering

نویسندگان

  • Adriane Boyd
  • Marion Zepf
  • Walt Detmar Meurers
چکیده

We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect determiner, and incorrect preposition. Approximately two-thirds of the errors annotated in HOO 2012 training and test data fall into these three categories. To improve our approach, we developed a missing determiner detector and incorporated word clustering (Brown et al., 1992) into the n-gram prediction approach.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Informing Determiner and Preposition Error Correction with Word Clusters

We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect de...

متن کامل

NAIST at the HOO 2012 Shared Task

This paper describes the Nara Institute of Science and Technology (NAIST) error correction system in the Helping Our Own (HOO) 2012 Shared Task. Our system targets preposition and determiner errors with spelling correction as a pre-processing step. The result shows that spelling correction improves the Detection, Correction, and Recognition Fscores for preposition errors. With regard to preposi...

متن کامل

VTEX Determiner and Preposition Correction System for the HOO 2012 Shared Task

This paper describes the system has been developed for the HOO 2012 Shared Task. The task was to correct determiner and preposition errors. I explore the possibility of learning error correcting rules from the given manually annotated data using features such as word length and word endings only. Furthermore, I employ error correction ranking based on the ratio of the sentence probabilities usi...

متن کامل

Memory-based Grammatical Error Correction

We describe the ’TILB’ team entry for the CONLL-2013 Shared Task. Our system consists of five memory-based classifiers that generate correction suggestions for center positions in small text windows of two words to the left and to the right. Trained on the Google Web 1T corpus, the first two classifiers determine the presence of a determiner or a preposition between all words in a text. The sec...

متن کامل

A Classifier-Based Approach to Preposition and Determiner Error Correction in L2 English

In this paper, we present an approach to the automatic identification and correction of preposition and determiner errors in nonnative (L2) English writing. We show that models of use for these parts of speech can be learned with an accuracy of 70.06% and 92.15% respectively on L1 text, and present first results in an error detection task for L2 writing.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012