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Readme_OI.md

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Dependencies

The code relies on several 3rd party libraries:

  • numpy
  • scipy
  • scikit-learn
  • dill
  • joblib
  • nltk
  • sqlitedict

In addition the following code (which has its own dependencies) is necessary for performing distributional inference:

  • DiscoUtils: https://github.com/tttthomasssss/DiscoUtils

Installation

Apart from DiscoUtils which needs to be installed manually, all requirements as well as the codebase itself can be installed with:

cd path/to/apt-toolkit
pip install -e .

Resources

Vectors from the paper Improving Semantic Composition with Offset Inference will be posted on github - unfortunately they were too large for the ACL submission system.

Usage

Loading vectors:

from apt_toolkit.utils import vector_utils

vectors = vector_utils.load_vector_cache('path/to/vectors', filetype='dill') # Loads the higher-order dependency-typed vectors as a `dict` of `dicts`

Creating Offset Representations:

from apt_toolkit.composition import mozart
from apt_toolkit.distributional_inference import distributional_inference
from apt_toolkit.utils import vector_utils

from discoutils.thesaurus_loader import Vectors

'''
Loads a standard set of vectorised APTs, creates some offsets and adds them back to the lexicon
This is one way - and perhaps the simplest way - of doing creating neighbours for offsets
'''

# Load Vectors
vectors = vector_utils.load_vector_cache('path/to/vectors', filetype='dill')

noun_vector = vectors['quantity']
adj_vector = vectors['large']

# Offset and add the resulting representation back to the lexicon 
offset_vector_adj = vector_utils.create_offset_vector(adj_vector, 'amod')
vectors['__OFFSET_amod_large'] = offset_vector_adj

# Can also offset the noun vector or any other vectors and add them back to the lexicon
offset_vector_noun = vector_utils.create_offset_vector(noun_vector, '!amod')
vectors['__OFFSET_!amod_quantity'] = offset_vector_noun

# Store vectors back to file
vector_utils.save_vector_cache(vectors, 'path/to/offsets', filetype='dill')

Composing Vectors with Offset Inference:

from apt_toolkit.distributional_inference import distributional_inference
from apt_toolkit.utils import vector_utils

# Load Vectors with previously added offset representations
vectors = vector_utils.load_vector_cache('path/to/offsets', filetype='dill')

adj_vector = vectors['exciting']

# Use offset Inference to enrich the noun-view of the adjective "exciting"
exciting_offset = vector_utils.create_offset_vector(adj_vector, 'amod')
rich_exciting_offset = distributional_inference.static_top_n(vectors=vectors, words=['__OFFSET_amod_exciting'], num_neighbours=20)

# Use the standard DI algorithm to infer unobserved co-occurrence features for the noun "book"
rich_book = distributional_inference.static_top_n(vectors=vectors, words=['book'], num_neighbours=20)

# Now the two vectors can be composed
composed_vector = mozart.intersect_apts(rich_exciting_offset, rich_book)