Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Applied Natural Language Processing
Applied Natural Language Processing
Artificial Intelligence
Unsupervised learning of the morphology of a natural language
Computational Linguistics
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Acquisition of Morphology of an Indic Language from Text Corpus
ACM Transactions on Asian Language Information Processing (TALIP)
A naive theory of affixation and an algorithm for extraction
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Development of prototype morphological analyzer for the South Indian language of Kannada
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
ParaMor and Morpho challenge 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Morphological lexicon extraction from raw text data
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Poor man’s stemming: unsupervised recognition of same-stem words
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
An improved stemming approach using HMM for a highly inflectional language
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
| Hi-index | 0.00 |
Words play a crucial role in aspects of natural language understanding such as syntactic and semantic processing. Usually, a natural language understanding system either already knows the words that appear in the text, or is able to automatically learn relevant information about a word upon encountering it. Usually, a capable system---human or machine, knows a subset of the entire vocabulary of a language and morphological rules to determine attributes of words not seen before. Developing a knowledge base of legal words and morphological rules is an important task in computational linguistics. In this paper, we describe initial experiments following an approach based on unsupervised learning of morphology from a text corpus, especially developed for this purpose. It is a method for conveniently creating a dictionary and a morphology rule base, and is, especially suitable for highly inflectional languages like Assamese. Assamese is a major Indian language of the Indic branch of the Indo-European family of languages. It is used by around 15 million people.