Most deaf children and adults struggle to read, but some deaf individuals do become highly proficient readers. There is disagreement about the specific causes of reading difficulty in the deaf population, and consequently, disagreement about the effectiveness of different strategies for teaching reading to deaf children. Much of the disagreement surrounds the question of whether deaf children r...

English as a Second Language (ESL) learners’ writings contain various grammatical errors. Previous research on automatic error correction for ESL learners’ grammatical errors deals with restricted types of learners’ errors. Some types of errors can be corrected by rules using heuristics, while others are difficult to correct without statistical models using native corpora and/or learner corpora...

Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin's grammars have flexible r...

This paper explores the generation of artificial errors for correcting grammatical mistakes made by learners of English as a second language. Artificial errors are injected into a set of error-free sentences in a probabilistic manner using statistics from a corpus. Unlike previous approaches, we use linguistic information to derive error generation probabilities and build corpora to correct sev...

This paper introduces three types of Statistical Machine Translation (SMT) output errors that would require grammatical knowledge for prevention. The first type is due to words that are negative in meaning but not in form. Problems arise when the negative forms are obligatory in target languages. The second type of errors is derived from the rigidity of pattern phrases or correlatives which do ...

We present an unsupervised method for detecting grammatical errors by inferring negative evidence from edited textual corpora. The system was developed and tested using essay-length responses to prompts on the Test of English as a Foreign Language (TOEFL). The errorrecognition system, ALEK, performs with about 80% precision and 20% recall.