Dataset

This dataset was created by Suraj

Contents

for word embedding

Context

https://www.adityathakker.com/wp-content/uploads/2017/06/word-embeddings-994x675.png" alt="word2vec"> Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.

Content

Existing Word2Vec Embeddings. GoogleNews-vectors-negative300.bin glove.6B.50d.txt glove.6B.100d.txt glove.6B.200d.txt glove.6B.300d.txt

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

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We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach on three datasets from different languages.

The russian model.

$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)' Python 3.5.2 :: Continuum Analytics, Inc. Name: tensorflow Version: 0.12.1 Name: numpy Version: 1.12.0 Name: scipy Version: 0.18.1 Name: scikit-learn Version: 0.18.1 Name: gensim Version: 0.13.4.1

The english-combined model has been trained using the well-known word embeddings dataset based on Google News: GoogleNews-vectors-negative300.bin on EVALution, BLESS, K&H+N, ROOT09 combined. The english-evalution model is traned on EVALution only.

$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)' Python 3.5.2 :: Anaconda custom (64-bit) Name: tensorflow Version: 0.12.1 Name: numpy Version: 1.11.3 Name: scipy Version: 0.18.1 Name: scikit-learn Version: 0.18.1 Name: gensim Version: 0.13.4.1