sinr 1.3.3

Build word and graph embeddings based on community detection in graphs.

SINr is an open-source tool to efficiently compute graph and word embeddings. Its aim is to provide sparse interpretable vectors from a graph structure. The dimensions of the vector produced are related to the community structure detected in the graph. By leveraging the relative connection of vertices to communities, SINr builds an interpretable space. SINr is focused on providing tools to build and interpret the embeddings produced.

SINr is a Python module relying on Networkit for the graph structure and community detection. SINr also provides efficient implementations to extract word co-occurrence graphs from large text corpora. One of the strength of SINr is its ability to work with text and produce interpretable word embeddings that are competitive with similar approaches. For more details on the performances of SINr on downstream evaluation tasks, please refer to the Publications section.

Requirements

• As SINr relies on libraries implemented using C/C++, a modern C++ compiler is required.

• OpenMP (required for Networkit and compiling SINr’s Cython)

• Python 3.9

• Pip

• Cython

• Conda (recommended)

Install

SINr can be installed through pip.

pip

conda activate sinr # activate conda environment
pip install sinr

Usage example

To get started using SINr to build graph and word embeddings, have a look at the notebook directory.

Here is a minimum working example of SINr

import nltk # For textual resources

import sinr.text.preprocess as ppcs
from sinr.text.cooccurrence import Cooccurrence
from sinr.text.pmi import pmi_filter
import sinr.graph_embeddings as ge
import sinr.text.evaluate as ev

# Get a textual corpus
# For example, texts from the Project Gutenberg electronic text archive,
# hosted at http://www.gutenberg.org/
nltk.download('gutenberg')
gutenberg = nltk.corpus.gutenberg # contains 25,000 free electronic books
file = open("my_corpus.txt", "w")
file.write(gutenberg.raw())
file.close()

# Preprocess corpus
vrt_maker = ppcs.VRTMaker(ppcs.Corpus(ppcs.Corpus.REGISTER_WEB,
                                      ppcs.Corpus.LANGUAGE_EN,
                                      "my_corpus.txt"),
                                      ".", n_jobs=8)
vrt_maker.do_txt_to_vrt()
sentences = ppcs.extract_text("my_corpus.vrt", min_freq=20)

# Construct cooccurrence matrix
c = Cooccurrence()
c.fit(sentences, window=5)
c.matrix = pmi_filter(c.matrix)
c.save("my_cooc_matrix.pk")

# Train SINr model
model = ge.SINr.load_from_cooc_pkl("my_cooc_matrix.pk")
commu = model.detect_communities(gamma=10)
model.extract_embeddings(commu)

# Construct SINrVectors to manipulate the model
sinr_vec = ge.InterpretableWordsModelBuilder(model,
                                             'my_sinr_vectors',
                                             n_jobs=8,
                                             n_neighbors=25).build()
sinr_vec.save()

# Sparsify vectors for better interpretability and performances
sinr_vec.sparsify(100)

# Evaluate the model with the similarity task
print('\nResults of the similarity evaluation :')
print(ev.similarity_MEN_WS353_SCWS(sinr_vec))

# Explore word vectors and dimensions of the model
print("\nDimensions activated by the word 'apple' :")
print(sinr_vec.get_obj_stereotypes('apple', topk_dim=5, topk_val=3))

print("\nWords similar to 'apple' :")
print(sinr_vec.most_similar('apple'))

# Load an existing SinrVectors object
sinr_vec = ge.SINrVectors('my_sinr_vectors')
sinr_vec.load()

Documentation

The documentation for SINr is available online.

Contributing

Pull requests are welcome. For major changes, please open an issue first to disccus the changes to be made.

License

Released under CeCILL 2.1, see LICENSE for more details.

Publications

SINr is currently maintained at the University of Le Mans. If you find SINr useful for your own research, please cite the appropriate papers from the list below. Publications can also be found on publications page in the documentation.

Initial SINr paper, 2021

• Thibault Prouteau, Victor Connes, Nicolas Dugué, Anthony Perez, Jean-Charles Lamirel, et al.. SINr: Fast Computing of Sparse Interpretable Node Representations is not a Sin!. Advances in Intelligent Data Analysis XIX, 19th International Symposium on Intelligent Data Analysis, IDA 2021, Apr 2021, Porto, Portugal. pp.325-337, ⟨10.1007/978-3-030-74251-5_26⟩. ⟨hal-03197434⟩

Interpretability of SINr embedding

• Thibault Prouteau, Nicolas Dugué, Nathalie Camelin, Sylvain Meignier. Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus. LREC 2022, Jun 2022, Marseille, France. ⟨hal-03770444⟩

Sparsity of SINr embedding

• Simon Guillot, Thibault Prouteau, Nicolas Dugué. Sparser is better: one step closer to word embedding interpretability. IWCS 2023, Nancy, France. ⟨hal-04321407⟩

Filtering dimensions of SINr embedding

• Anna Béranger, Nicolas Dugué, Simon Guillot, Thibault Prouteau. Filtering communities in word co-occurrence networks to foster the emergence of meaning. Complex Networks 2023, Menton, France. ⟨hal-04398742⟩