A Bambara Tonalization System for Word Sense Disambiguation Using Differential Coding, Segmentation and Edit Operation Filtering

Yu-Cheng Liu (1) , Damien Nouvel (1)

1 ERTIM - Équipe de Recherche en Textes, Informatique, Multilinguisme

In many languages such as Bambara or Arabic, tone markers (diacritics) may be written but are actually often omitted. NLP applications are confronted to ambiguities and subsequent difficulties when processing texts. To circumvent this problem , tonalization may be used, as a word sense disambiguation task, relying on context to add diacritics that partially disam-biguate words as well as senses. In this paper , we describe our implementation of a Bambara tonalizer that adds tone markers using machine learning (CRFs). To make our tool efficient, we used differential coding , word segmentation and edit operation filtering. We describe our approach that allows tractable machine learning and improves accuracy: our model may be learned within minutes on a 358K-word corpus and reaches 92.3% accuracy.

Domaines: Apprentissage [cs.LG]; Informatique et langage [cs.CL]

Yu-Cheng Liu, Damien Nouvel. A Bambara Tonalization System for Word Sense Disambiguation Using Differential Coding, Segmentation and Edit Operation Filtering. The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), Nov 2017, Taipei, Taiwan. pp.694 - 703. ⟨hal-01685393⟩