Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …

Distributional models represent a word through the contexts in which it has been observed.
They can be used to predict similarity in meaning, based on the distributional hypothesis …

The ability to accurately represent sentences is central to language understanding. We
describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network …

Semantic word spaces have been very useful but cannot express the meaning of longer
phrases in a principled way. Further progress towards understanding compositionality in …

Many machine learning algorithms require the input to be represented as a fixed length
feature vector. When it comes to texts, one of the most common representations is bag-of …

Single-word vector space models have been very successful at learning lexical information.
However, they cannot capture the compositional meaning of longer phrases, preventing …

The lexicon of any natural language encodes a huge number of distinct word meanings. Just
to understand this article, you will need to know what thousands of words mean. The space …

Abstract The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates …

Text classification methods for tasks like factoid question answering typically use manually
defined string matching rules or bag of words representations. These methods are …

This chapter describes how vector space models have been used for document retrieval.
These document‐based models represent the meaning, or topic, of a whole document. It …