Co-learning of Word Representations and Morpheme Representations

By Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, Tie-Yan Liu. 2014

98 citations recorded by the source corpus.

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

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

In Natural Language Processing

1. Alexander M. Rush
2. Yoon Kim
3. Mirella Lapata
4. Yonatan Belinkov
5. David A. Sontag
6. Noah A. Smith
7. Nanyun Peng
8. Kyunghyun Cho

In Representation & Self-Supervised Learning

1. Adam Lopez
2. David A. Sontag
3. Mark Dredze
4. Yacine Jernite
5. Alexander M. Rush
6. Clara Vania
7. Nanyun Peng
8. Yoon Kim

Citations in the corpus

• Character-Aware Neural Language Models (2015, background): “epresent words. A specific class of FNLMs leverages morphemic information by viewing a word as a function of its (learned) morpheme embeddings (Luong, Socher, and Manning 2013; Botha and Blunsom 2014; Qui et al. 2014). For example Luong, Socher, and Manning (2013) apply a recursive neural network ove”
• Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings (2015, background): “While work has explored different embeddings for Chinese (Liu et al., 2014; Sun et al., 2014; Qiu et al., 2014; Chen et al., 2015), their inclusion in downstream tasks, such as NER, remains untested.”
• From Characters to Words to in Between: Do We Capture Morphology? (2017, background): “…of characters (Ling et al., 2015; Kim et al., 2016), character n-grams (Sperr et al., 2013; Wieting et al., 2016; Bojanowski et al., 2016; Botha and Blun-som, 2014), bytes (Plank et al., 2016; Gillick et al., 2016), or combinations thereof (Santos and Zadrozny, 2014; Qiu et al., 2014).”