4,099 research outputs found
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
A large list of confusion sets for spellchecking assessed against a corpus of real-word errors
One of the methods that has been proposed for dealing with real-word errors (errors that occur when a correctly spelled word is substituted for the one intended) is the "confusion-set" approach - a confusion set being a small group of words that are likely to be confused with one another. Using a list of confusion sets drawn up in advance, a spellchecker, on finding one of these words in a text, can assess whether one of the other members of its set would be a better fit and, if it appears to be so, propose that word as a correction. Much of the research using this approach has suffered from two weaknesses. The first is the small number of confusion sets used. The second is that systems have largely been tested on artificial errors. In this paper we address these two weaknesses. We describe the creation of a realistically sized list of confusion sets, then the assembling of a corpus of real-word errors, and then we assess the potential of that list in relation to that corpus
Misspelling Oblivious Word Embeddings
In this paper we present a method to learn word embeddings that are resilient
to misspellings. Existing word embeddings have limited applicability to
malformed texts, which contain a non-negligible amount of out-of-vocabulary
words. We propose a method combining FastText with subwords and a supervised
task of learning misspelling patterns. In our method, misspellings of each word
are embedded close to their correct variants. We train these embeddings on a
new dataset we are releasing publicly. Finally, we experimentally show the
advantages of this approach on both intrinsic and extrinsic NLP tasks using
public test sets.Comment: 9 Page
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