Indian Language Spellchecker Development for OpenOffice.org

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This document summarizes a presentation about developing spell checking dictionaries for Indic languages in OpenOffice.org. It discusses how Hunspell works as the spell checking library in OOo, how to build dictionaries in the required .dic and .aff file formats, issues in developing dictionaries for Indian languages like availability of word lists and rule generation, tips for building dictionaries, testing and evaluating performance, and future work needed in Hunspell to better support Indic language spell checking in OOo.

Technology

Development of Indic Language Spell Checking
Dictionary for OOo

K G Sulochana
G Jaganadh
C-DAC Thiruvananthapuram

FossConf 2008 Chennai

Talk Summery


Introduction

OpenOffice.org

Hunspell

Building Dictionaries

Issues

Tips for Building Dictionaries

Evaluation of Performance

Conclusion
FossConf 2008 Chennai

Introduction

Localization
Spell checking
Spell checker Development in Indian Languages

FossConf 2008 Chennai

OpenOffice.org

Free and Open Source Office Suite
Collaborative Development
Available with Interface in Indian Languages
Future Office Suite of India

FossConf 2008 Chennai

Hunspell

Spell checking library in OOo
Free and Open Source
Capable of Handling Complex Languages
Unicode Support
Compound Handling

FossConf 2008 Chennai

Building Dictionaries

Format of Hunspell Dictionaries
Word list .dic file
Rule Base or Affix list .aff file
How to prepare .dic file ?
How to prepare .aff file ?
Building OOo with your .dic file and .aff file

FossConf 2008 Chennai

Issues

Availability of Word list
Rule Generation
Sandhi Handling
Tuning Compound Handling part for Indian Languages

FossConf 2008 Chennai

Tips

Tips and tricks for generating word list
Tips and Tricks for rule base development

FossConf 2008 Chennai

Testing and Evaluation

Testing of spell checker
Performance evaluation
Quality ensuring

FossConf 2008 Chennai

Towards Future

Works to do in Hunspell for Indic Language Spell
Checking in OOo
Setting Up User Groups of Help Support and
Development

FossConf 2008 Chennai

Concluding Remarks

FossConf 2008 Chennai

In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.

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