On the Use of Word Embeddings Alone to Represent Natural Language Sequences
By Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin. 2018
9 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.
- Chunyuan Li
- Dinghan Shen
- Lawrence Carin
- Yizhe Zhang
- Guoyin Wang
- Ricardo Henao
- Wenlin Wang
- Qinliang Su
Citations in the corpus
- Joint Embedding of Words and Labels for Text Classification (2018, methodology): “We compare with a variety of methods, including (i) the bag-of-words in (Zhang et al., 2015); (ii) sophisticated deep CNN/RNN models: large/small word CNN, LSTM reported in (Zhang et al., 2015; Dai and Le, 2015) and deep CNN (29 layer) (Conneau et al., 2017); (iii) simple compositional methods: fast”
- Predictive Embeddings for Hate Speech Detection on Twitter (2018, methodology): “In contrast to the methods described above, our approach relies on a simple word embedding (SWEM)-based architecture (Shen et al., 2018), reducing the number of required parameters and length of training required, while still yielding improved performance and resilience across related classification…”
- NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018, background): “For natural language processing (NLP), although significant research has been made to learn continuous deep representations for words or documents (Mikolov et al., 2013; Kiros et al., 2015; Shen et al., 2018), discrete neural representations have been mainly explored in learning word embeddings (Shu”