Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither

By Reut Tsarfaty, Djamé Seddah, Yoav Goldberg, Sandra Kübler, Yannick Versley, Marie Candito, Jennifer Foster, Ines Rehbein, Lamia Tounsi. 2010

151 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.

1. Djamé Seddah
2. Sandra Kübler
3. Reut Tsarfaty
4. Mitchell Stern
5. André F. T. Martins

Citations in the corpus

• MLSUM: The Multilingual Summarization Corpus (2020, neutral): “This could find an explanation in the morphology of the two languages: in statistical parsing, Tsarfaty et al. (2010) considered German to be very sensitive to word order, due to its rich morphology, as opposed to French.”
• Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages (2014, methodology): “One of the common problems when dealing with morphologically rich languages (MRLs) is lexical data sparseness due to the high level of variation in word forms (Tsarfaty et al., 2010; Tsarfaty et al., 2012c).”
• Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring (2019, unknown): “We note that both German and Czech (Tsarfaty et al., 2010) are morphologically rich languages, which leads to a very large vocabulary size for both languages, but especially for Czech (more than one million unique words for German, but three million unique words for Czech).”