Supervised Machine Learning for Hybrid Meter
Python
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
.gitignore
CLFL_Brill.py
CLFL_CRF_dev.py
CLFL_CRF_held-out.py
CLFL_inter-annotator_agreement.py
CLFL_mdf_classification.py
CLFL_ngram.py
CLFL_sum_stats.py
LICENSE
README.md

README.md

NAACL-CLFL 2016

Title: Supervised Machine Learning for Hybrid Meter
Authors: Alex Estes and Christopher Hench

Abstract:
Following classical antiquity, European poetic meter was complicated by traditions negotiating between the prosodic stress of vernacular dialects and a classical system based on syllable length. Middle High German (MHG) epic poetry found a solution in a hybrid qualitative and quantitative meter. We develop a CRF model to predict the metrical values of syllables in MHG epic verse, achieving an F-score of .894 on 10-fold cross-validated development data (outperforming several baselines) and .904 on held-out testing data. The method used in this paper presents itself as a viable option for other literary traditions, and as a tool for subsequent genre or author analysis.

Data and source code for paper. For an updated model, see this repository.

Dependencies:

  • sklearn (pip install scikit-learn)
  • nltk (pip install nltk)
  • pycrfsuite (pip install python-crfsuite)