In Defense of Word Embedding for Generic Text Representation

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Character-level Convolutional Networks for Text Classification (2015, critical): “Bag-of-means is a misuse of word2vec [20].”
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (2017, background): “(Lev et al., 2015) also presented an early approach to obtain compositional embeddings from word vectors.”
RNN Fisher Vectors for Action Recognition and Image Annotation (2015, background): “State of the art results are obtained in two central but distant tasks, which both rely on sequences: video action recognition and image annotation.”
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings (2018, methodology): “Following the general approach piloted here, analysts could use word embedding models to compare the cultural systems represented by literary genres, texts produced by distinct authors, or texts written in different languages (Lev, Klein, and Wolf 2015).”