Statistical Parsing of Spanish and Data Driven Lemmatization

Joseph Le Roux (1) , Benoît Sagot (2) , Djamé Seddah (2, 3)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 ALPAGE - Analyse Linguistique Profonde à Grande Echelle ; Large-scale deep linguistic processing
3 ISHA - Institut des Sciences Humaines Appliquées

Although parsing performances have greatly improved in the last years, grammar inference from treebanks for morphologically rich lan- guages, especially from small treebanks, is still a challenging task. In this paper we in- vestigate how state-of-the-art parsing perfor- mances can be achieved on Spanish, a lan- guage with a rich verbal morphology, with a non-lexicalized parser trained on a treebank containing only around 2,800 trees. We rely on accurate part-of-speech tagging and data- driven lemmatization in order to cope with lexical data sparseness. Providing state-of- the-art results on Spanish, our methodology is applicable to other languages.

Domaines: Informatique et langage [cs.CL]

Joseph Le Roux, Benoît Sagot, Djamé Seddah. Statistical Parsing of Spanish and Data Driven Lemmatization. ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages (SP-Sem-MRL 2012), Jul 2012, Jeju, South Korea. 6 p. ⟨hal-00702496⟩