= DELTA-H Online entropy-based model of lexical category acquisition. Grzegorz Chrupala and Afra Alishahi = INSTALL Install the Haskell Platform: http://hackage.haskell.org/platform/ On linux, the following command will install the delta-h executable in the bin directory: cabal install --prefix=`pwd` = USAGE The data directory has an example input file data/goat.txt The other files are CHILDES. To induce a model (i.e. a set of clusters), execute the following: > ./bin/delta-h learn '[-12,0,12]' data/goat.txt The argument '[-12,0,12]' specifies the features to be used (in this case preceding bigram, focus word, and following bigram. Feature ids can be inspected in the source file src/Entropy/Features.hs The model will be stored in data/goat.txt.[-12,0,12].learn.model You can display the model in a human-readable format with: > ./bin/delta-h display data/goat.txt.[-12,0,12].learn.model The learned model can also be used to label input data, without further learning: > ./bin/delta-h label True True data/goat.txt.[-12,0,12].learn.model < \ data/goat.txt The first argument specifies whether to use focus word for labeling, the second argument whether to avoid outputting new cluster ids (not in the model). There is also a command which test the learned model on the word prediction task: > ./bin/delta-h eval-mrr True True data/goat.txt.[-12,0,12].learn.model < \ data/goat.txt The first argument specifies whether to marginalize over all cluster assignments, the second whether to output detailed information. The semantic property prediction task can be run with the eval-sem command: > ./bin/delta-h eval-sem False data/lexicon TRAIN.pos TRAIN.cluster \ TEST.pos TEST.cluster The meaning of the arguments to this command: False - do not produce verbose output data/lexicon - semantic property lexicon file (generated from Wordnet) TRAIN.pos - POS tagged train data TRAIN.cluster - train data labeled with cluster IDs (use the label command to generate it) TEST.pos - POS tagged test data TEST.cluster - test data labeled with cluster IDs (use the label command to generate it) = SOURCES There are some other (currently undocumented) commands: inspect src/Main.hs The main part of the model is implemented in src/Entropy/Algorithm.hs.