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SeaQuBe

Semantic Quality Benchmark for Word Embeddings, i.e. Natural Language Models in Python. Acronym SeaQuBe or seaqube.

This python framework provides several text augmentation implementations and word embedding quality evaluation methods. It is designed to fit in your machine learning pipeline. The BaseAugmentation class provides the same api as the python package nlpaug, so that this packages can used together smoothly. However BaseAugmentation provides also other methods. Detailed examples see beneath.

SeaQuBe provides also a toolkit to wrap a trained nlp model to a nice interactive tool.

Travis build Status code:qualityPyPI version

Features

  • Text Data Augmentation
  • Chaining and Reducing of Text Data Augmentations
  • Word Embedding Quality Methods
  • Interactive NLM Model Wrapper

Demo

Augmentation

Level Augmenter Description
Character QwertyAugmentation Simulate keyboard distance error
Corpus UnigramAugmentation Replace ubiquitous words with other ubiquitous words
Word Active2PassiveAugmentation Change surface of document using an simple active-to-passive transformer
Word EDAAugmentation Augment document using the EDA algorithm
Word EmbeddingAugmentation Replace similar word using WordNet
Word TranslationAugmentation Change surface of document using translation and back-translation (with GoogleTranslate)

Augmentation Chainer

The streaming feature of augmentation is implemented in the AugmentationStreamer class. One Reduceing class exist, more can implemented extending the BaseReduction class.

Action Class Description
Streaming AugmentationStreamer Run augmentation for each document through all chained augmentations.
Reducing UniqueCorpusReduction Getting a list of documents, only unique documents are returned.

Word Embedding Evaluation

Method Description
WordAnalogyBenchmark This method benchmark how go relations of the type: a is to b as c is to d can be solved correctly.
WordSimilarityBenchmark This methods compares the similarity of a word pair, calculated by a model with a human estimated similarity score.
WordOutliersBenchmark This method benchmark how good a outlier of a group of words can be detected.
SemanticWordnetBenchmark Based on the WordNet graph, the goodnes of the semantic / similarity of a nlp model is benchmarked.

Installation

SeaQuBe can be installed from PyPip using: pip install seaqube or run in the main directory: python setup.py install.

External Dependencies

Some external dependencies are not installed automatically, but seaqube or nltk might throw errors with an instruction what to do. For example seqube might ask you to run:

python -c "from seaqube import download;download('vec4ir')"

Quick Demo

from seaqube.augmentation.word import Active2PassiveAugmentation, EDAAugmentation, TranslationAugmentation, EmbeddingAugmentation
translate = TranslationAugmentation(max_length=2)
translate.doc_augment(['This', 'is', 'a', 'tokenized', 'corpus'])

Setup Dev Environment

TODO

About

Semantic Quality Benchmark for Word Embeddings, i.e. Natural Language Models in Python. Acronym `SeaQuBe` or `seaqube`.

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