Skip to main content

Tool for creating document features

Project description

Vectors of Locally Aggregated Concepts (VLAC)

PyPI - Status PyPI - Python PyPI - Python


As illustrated in the Figure below, VLAC clusters word embeddings to create k concepts. Due to the high dimensionality of word embeddings (i.e., 300) spherical k-means is used to perform the clustering as applying euclidean distance will result in little difference in the distances between samples. The method works as follows. Let wi be a word embedding of size D assigned to cluster center ck. Then, for each word in a document, VLAC computes the element-wise sum of residuals of each word embedding to its assigned cluster center. This results in k feature vectors, one for each concept, and all of size D. All feature vectors are then concatenated, power normalized, and finally, l2 normalization is applied. For example, if 10 concepts were to be created out of word embeddings of size 300 then the resulting document vector would contain 10 x 300 values.


Tested in python 3.5.4.

# Train model and transform collection of documents
vlac_model = VLAC(documents=train_docs, model=model, oov=False)
vlac_features, kmeans = vlac_model.fit_transform(num_concepts=30)

# Create features new documents
vlac_model = VLAC(documents=train_docs, model=model, oov=False)
test_features = vlac_model.transform(kmeans=kmeans)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vlac- (3.6 kB view hashes)

Uploaded source

Built Distribution

vlac- (7.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page