paper-with-me

Papers

An In-Depth Comparison of 14 Spelling Correction Tools on a Common Benchmark

2020-05-01 · LREC 2020 5 · Markus N{\"a}ther

Determining and correcting spelling and grammar errors in text is an important but surprisingly difficult task. There are several reasons why this remains challenging. Errors may consist of simple typing errors like deleted, substituted, or wrongly inserted letters, but may also consist of word confusions where a word was replaced by another one. In addition, words may be erroneously split into two parts or get concatenated. Some words can contain hyphens, because they were split at the end of a line or are compound words with a mandatory hyphen. In this paper, we provide an extensive evaluation of 14 spelling correction tools on a common benchmark. In particular, the evaluation provides a detailed comparison with respect to 12 error categories. The benchmark consists of sentences from the English Wikipedia, which were distorted using a realistic error model. Measuring the quality of an algorithm with respect to these error categories requires an alignment of the original text, the distorted text and the corrected text provided by the tool. We make our benchmark generation and evaluation tools publicly available.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Spelling Correction

Similar Papers 제목 키워드 기반

English Language Spelling Correction as an Information Retrieval Task Using Wikipedia Search Statistics

2022-06-01 · LREC 2022 6 · Kyle Goslin, Markus Hofmann

Spelling correction utilities have become commonplace during the writing process, however, many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction. Many terms, ac…

Information RetrievalRetrievalSpelling Correction

Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings

2017-08-01 · WS 2017 8 · Pieter Fivez, Simon {\v{S}}uster, Walter Daelemans

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them accord…

Spelling Correction

Spelling Correction for Russian: A Comparative Study of Datasets and Methods

2021-09-01 · RANLP 2021 9 · Alla Rozovskaya

We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian. The first corpus is a dataset of Russian social media data that was…

Cross-corpusMachine TranslationRe-RankingSpelling Correction+1

AraSpell: A Deep Learning Approach for Arabic Spelling Correction

2024-05-11 · Mahmoud Salhab, Faisal Abu-Khzam

Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell,"…

Deep LearningSpelling Correction

MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction

2022-10-21 · Wangjie Jiang, Zhihao Ye, Zijing Ou, Ruihui Zhao 외

Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensive use in many applications, like search…

Optical Character RecognitionOptical Character Recognition (OCR)Spelling Correction