In this project we make the first steps toward understanding typological differences of English, Spanish and Basque poetic rhythm with the aim of creating a language independent scansion system.
We will soon release some specific scripts to read these corpora.
There are specific implementations to get some necessary features, such as:
- isheavy: This function returns true if the syllable is heavy or not according to a heuristic.
- Lexical stress: We have implemented functions to get the lexical stress for each language.
- Part-of-speech tags: We have used pretrained models for POS-tagging.
- English: Hidden Markov Models trained on the WSJ section of the Penn Treebank corpus
- Spanish: Ixa-pipes trained on the Ancora corpus
- Basque: Ixa-pipes trained on the Universal Dependencies corpus
- In the English corpus syllables are divided. In the Spanish and Basque corpora, as words are not syllabified, we are using a syllabification algorithm based on the sonority hierarchy and maximum onset principle.
- Averaged Perceptron
- Hidden Markov Models
- Conditional Random Fields and a wrapper for Python
- Double Bi-LSTM + CRF
- Encoder-Decoder architecture (machine_translation example at blocks-examples)
- RNNLM (char-rnn, or currently, torch-rnn should be used)
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