Searching for Discriminative Metadata of Heterogenous Corpora

Gaël Guibon (1) , Isabelle Tellier (1) , Sophie Prévost (1) , Mathieu Constant (2) , Kim Gerdes (3)

1 Lattice - Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094
2 UPE - Université Paris-Est
3 LPP - LPP - Laboratoire de Phonétique et Phonologie - UMR 7018

In this paper, we use machine learning techniques for part-of-speech tagging and parsing to explore the specificities of a highly heterogeneous corpus. The corpus used is a treebank of Old French made of texts which differ with respect to several types of metadata: production date, form (verse/prose), domain , and dialect. We conduct experiments in order to determine which of these metadata are the most discriminative and to induce a general methodology .

Mots clés: machine learning; heterogeneous corpus exploration; Old French; dependency parsing; POS labelling

Domaines: Linguistique; Informatique et langage [cs.CL]; Traitement du texte et du document; Interface homme-machine [cs.HC]

Gaël Guibon, Isabelle Tellier, Sophie Prévost, Mathieu Constant, Kim Gerdes. Searching for Discriminative Metadata of Heterogenous Corpora. Fourteenth International Workshop on Treebanks and Linguistic Theories (TLT14), Dec 2015, Varsovie, Poland. pp.72-82. ⟨hal-01250981⟩