Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

NLPIR SEMINAR Y2019#21

INTRO

In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

Arrangement

This week’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
  2. The lecturer is Changhe Li, the paper’s title is Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning.
  3. Zhaoyang Wang give the presentation of his work .
  4. The seminar will be hosted by ShenLi.
  5. Attachment is the paper of this seminar, please download in advance.

Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

Pravallika Etoori, Manoj Chinnakotla, Radhika Mamidi

Abstract

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resource-scarce and do not have such parallel data due to low volume of queries and nonexistence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resourcescarce languages. We propose a sequence-to-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.

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