Introduction to algorithms
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An alternate approach towards meaningful lyric generation in Tamil
CALC '10 Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
Computational creativity tools for songwriters
CALC '10 Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
"Poetic" statistical machine translation: rhyme and meter
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Unsupervised discovery of rhyme schemes
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Unsupervised rhyme scheme identification in hip hop lyrics using hidden markov models
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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This paper presents our on-going work to automatically generate lyrics for a given melody, for phonetic languages such as Tamil. We approach the task of identifying the required syllable pattern for the lyric as a sequence labeling problem and hence use the popular CRF++ toolkit for learning. A corpus comprising of 10 melodies was used to train the system to understand the syllable patterns. The trained model is then used to guess the syllabic pattern for a new melody to produce an optimal sequence of syllables. This sequence is presented to the Sentence Generation module which uses the Dijkstra's shortest path algorithm to come up with a meaningful phrase matching the syllabic pattern.