haskseg: Simple unsupervised segmentation model

[ bsd3, library, machine-learning, natural-language-processing, program ] [ Propose Tags ]

Implementation of the non-parametric segmentation model described in "Type-based MCMC" (Liang, Jordan, and Klein, 2010).


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Versions [faq] 0.1.0.0, 0.1.0.1
Dependencies ansi-terminal (>=0.8.0.4), array, base (>=4.7 && <5), bytestring (>=0.10.8.1), containers (>=0.5.10.2), exact-combinatorics (>=0.2.0.8), haskseg, logging-effect (>=1.3.2), monad-loops (>=0.4.3), MonadRandom (>=0.5.1.1), mtl (>=2.2.2), optparse-generic (>=1.2.2), random (>=1.1), random-shuffle (>=0.0.4), text (>=1.2.2), vector (>=0.12.0.1), zlib (>=0.6.1) [details]
License BSD-3-Clause
Copyright 2018 Tom Lippincott
Author Tom Lippincott
Maintainer tom@cs.jhu.edu
Category natural-language-processing, machine-learning
Home page https://github.com/githubuser/haskseg#readme
Uploaded by TomLippincott at Mon Dec 24 02:27:40 UTC 2018
Distributions NixOS:0.1.0.1
Executables haskseg
Downloads 218 total (18 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2018-12-24 [all 1 reports]

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Readme for haskseg-0.1.0.1

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haskmorph

Compiling

First install Stack somewhere on your PATH. For example, for ~/.local/bin:

wget https://get.haskellstack.org/stable/linux-x86_64.tar.gz -O -|tar xpfz - -C /tmp
cp /tmp/stack-*/stack ~/.local/bin
rm -rf /tmp/stack-*

Then, while in the directory of this README file, run:

stack build

The first time this runs will take a while, 10 or 15 minutes, as it builds an entire Haskell environment from scratch. Subsequent compilations are very fast.

Running

Invoke the program using Stack. To see available sub-commands, run:

stack exec -- haskmorph -h

To see detailed help, run e.g.:

stack exec -- haskmorph train -h