Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

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.