Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Word embeddings extract semantic features of words from large datasets of text.

Most embedding methods rely on a log-bilinear model to predict the occurrence

of a word in a context of other words.
Here we propose word2net, a method that

replaces their linear parametrization with neural networks.
For each term in the

vocabulary, word2net posits a neural network that takes the context as input and

outputs a probability of occurrence.
Further, word2net can use the hierarchical

organization of its word networks to incorporate additional meta-data, such as

syntactic features, into the embedding model.
For example, we show how to share

parameters across word networks to develop an embedding model that includes

part-of-speech information.
We study word2net with two datasets, a collection

of Wikipedia articles and a corpus of U.S. Senate speeches.
Quantitatively, we

found that word2net outperforms popular embedding methods on predicting held-

out words and that sharing parameters based on part of speech further boosts

performance.
Qualitatively, word2net learns interpretable semantic representations

and, compared to vector-based methods, better incorporates syntactic information.
Word2net is a novel method for learning neural network representations of words that can use syntactic information to learn better semantic features.