A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography

Searching by approximate personal-name matching

We discuss the design, building and evaluation of a method to access theinformation of a person, using his name as a search key, even if it has deformations. We present a similarity function, the DEA function, based on the probabilities of the edit operations accordingly to the involved letters and their position, and using a variable threshold. The efficacy of DEA is quantitatively evaluated, without human relevance judgments, very superior to the efficacy of known methods. A very efficient approximate search technique for the DEA function is also presented based on a compacted trie-tree structure.Postprint (published version

Sentence Level N-Gram Context Feature in Real-Word Spelling Error Detection and Correction: Unsupervised Corpus Based Approach

Spell checking is the process of finding misspelled words and possibly correcting them. Most of the modern commercial spell checkers use a straightforward approach to finding misspellings, which considered a word is erroneous when it is not found in the dictionary. However, this approach is not able to check the correctness of words in their context and this is called real-word spelling error. To solve this issue, in the state-of-the-art researchers use context feature at fixed size n-gram (i.e. tri-gram) and this reduces the effectiveness of model due to limited feature. In this paper, we address the problem of this issue by adopting sentence level n-gram feature for real-word spelling error detection and correction. In this technique, all possible word n-grams are used to learn proposed model about properties of target language and this enhance its effectiveness. In this investigation, the only corpus required to training proposed model is unsupervised corpus (or raw text) and this enables the model flexible to be adoptable for any natural languages. But, for demonstration purpose we adopt under-resourced languages such as Amharic, Afaan Oromo and Tigrigna. The model has been evaluated in terms of Recall, Precision, F-measure and a comparison with literature was made (i.e. fixed n-gram context feature) to assess if the technique used performs as good.  The experimental result indicates proposed model with sentence level n-gram context feature achieves a better result: for real-word error detection and correction achieves an average F-measure of 90.03%, 85.95%, and 84.24% for Amharic, Afaan Oromo and Tigrigna respectively. Keywords: Sentence level n-gram, real-word spelling error, spell checker, unsupervised corpus based spell checker DOI: 10.7176/JIEA/10-4-02 Publication date:September 30th 202

Automatic coding of short text responses via clustering in educational assessment

Automatic coding of short text responses opens new doors in assessment. We implemented and integrated baseline methods of natural language processing and statistical modelling by means of software components that are available under open licenses. The accuracy of automatic text coding is demonstrated by using data collected in the Programme for International Student Assessment (PISA) 2012 in Germany. Free text responses of 10 items with Formula responses in total were analyzed. We further examined the effect of different methods, parameter values, and sample sizes on performance of the implemented system. The system reached fair to good up to excellent agreement with human codings Formula Especially items that are solved by naming specific semantic concepts appeared properly coded. The system performed equally well with Formula and somewhat poorer but still acceptable down to Formula Based on our findings, we discuss potential innovations for assessment that are enabled by automatic coding of short text responses. (DIPF/Orig.