metaphor

Cross-lingual metaphor detection.

This tool is a partial implementation of the ACL'14 paper:

<a href="http://aclanthology.info/papers/metaphor-detection-with-cross-lingual-model-transfer">Metaphor Detection with Cross-Lingual Model Transfer</a>

</li>

Installation

This tool requires the following dependencies:

<blockquote> <ul> <li> Python 2.7 <a href="http://www.python.org/download/releases/2.7.3/">www.python.org/download/releases/2.7.3/</a> </li> <li> Natural Language Toolkit nltk <a href="http://nltk.org">nltk.org</a> </li> <li> A Python module for machine learning scikit-learn version 0.13 <a href="http://scikit-learn.org">scikit-learn.org</a> </li> <li> English dependency parser TurboParser version 2.1.0 <a href="https://www.ark.cs.cmu.edu/TurboParser">www.ark.cs.cmu.edu/TurboParser</a> </li> <li> Optionally: Russian dependency parser seman <a href="http://seman.sourceforge.net/">seman.sourceforge.net</a> </li> <li> Important: Uncompress resources/VSM/en-svd-de-64.txt.tar.gz file before running the tool

tar -zxvf en-svd-de-64.txt.tar.gz

</li> </ul> </blockquote>

The following script demonstrates commands needed for installing the dependencies: ```install_dependencies.sh```

<blockquote> <ul> Update from Apr 15, 2015: The new versions of scikit-learn are not backward compatible, and the tool breaks. To run the tool install older versions of scikit-learn, scipy and numpy in an virtual environment like this: (big thanks to Lingpeng Kong for figuring this out!)

1. Install virtualenv: sudo apt-get install python-virtualenv
2. Create a virualenv and install there older versions (see below)
3. Whenever you run the tool activate the virtualenv like this: source metaphor_env/bin/activate

virtualenv metaphor_env

source metaphor_env/bin/activate

pip install nltk

export NLTK_DATA="where you have the nltk_data, if not use nltk.download() to get them"

pip install nose==1.3.6

pip install numpy==1.8.1

pip install scipy==0.12.1

pip install scikit-learn==0.13

</ul> </blockquote>

Example

For usage example run the script: example.sh

Usage

Training:

Parameters:

1. relation type (*svo* or *an*)
2. format of the training files (*input-text* or *input-rel*)
3. path to metaphoric training file
4. path to literal training file

Examples:

./train.sh \
    svo \
    input-text \
    resources/TroFi/metaphorical.txt \
    resources/TroFi/literal.txt

./train.sh \
    an \
    input-rel \
    resources/AdjN/training_adj_noun_met_en.txt \
    resources/AdjN/training_adj_noun_nonmet_en.txt

Prediction for subject-verb-object (SVO) or adjective-noun (AN) syntactic relations:

Parameters:

1. relation type (*svo* or *an*)
1. path to relations input file (see examples in ./input/*) 

Examples:

./find_metaphors_rel.sh svo input/svo_mets.txt                 

Output format:

Tab-separated text file sentence-aligned to the input file with the following fields:

1. the original relation
2. predicted sentence label "M" or "L"
3. json-formatted metaphor candidates with labels

Prediction for raw text files split to sentences:

Parameters:

1. path to input file split to sentences

Examples:

./find_metaphors.sh path_to_file                       

Output format:

Tab-separated text file sentence-aligned to the input file with the following fields:

1. the original sentence
2. predicted sentence label "M" or "L"
3. json-formatted metaphor candidates with labels

Output example:

Care to offer some evidence that this bald assertion is true? <tab> M <tab> {"bald assertion": "M", "offer evidence": "M"}

Released resources:

<blockquote> <ul> <li> Adjective-Noun and Subject-Verb-Object English and Russian metaphorical and literal test sets along with annotator judgments:

./input/Datasets_ACL2014.xlsx

</li> <li> Adjective-Noun 884 metaphorical and 884 literal pairs used for model training:

./resouces/AdjN/*

</li> </ul> </blockquote>

This repository is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/