Morphological Disambiguation from Stemming Data
Morphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis. While linguistically curated finite state tools can be easily developed for morphological analysis, the morphological richness of the language allows many ambiguous analyses to be produced, requiring effective disambiguation. In this paper, we propose learning to morphologically disambiguate Kinyarwanda verbal forms from a new stemming dataset collected through crowd-sourcing. Using feature engineering and a feed-forward neural network based classifier, we achieve about 89% non-contextualized disambiguation accuracy. Our experiments reveal that inflectional properties of stems and morpheme association rules are the most discriminative features for disambiguation.
Code (0)
등록된 구현이 없습니다.
Tasks
Feature EngineeringMorphological AnalysisMorphological DisambiguationSimilar Papers 제목 키워드 기반
A set of open source tools for Turkish natural language processing
This paper introduces a set of freely available, open-source tools for Turkish that are built around TRmorph, a morphological analyzer introduced earlier in Coltekin (2010). The article first provides an update on the an…
Grapheme-to-Phoneme ConversionLemmatizationMorphological DisambiguationMorphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods
In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different da…
Morphological AnalysisMorphological DisambiguationMorphological TaggingGliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian
We present GliLem -- a novel hybrid lemmatization system for Estonian that enhances the highly accurate rule-based morphological analyzer Vabamorf with an external disambiguation module based on GliNER -- an open vocabul…
Information RetrievalLEMMALemmatizationNER+3A Morphology-aware Network for Morphological Disambiguation
Agglutinative languages such as Turkish, Finnish and Hungarian require morphological disambiguation before further processing due to the complex morphology of words. A morphological disambiguator is used to select the co…
Deep LearningFeature EngineeringMorphological AnalysisMorphological Disambiguation+2Character-Aware Neural Morphological Disambiguation
We develop a language-independent, deep learning-based approach to the task of morphological disambiguation. Guided by the intuition that the correct analysis should be {``}most similar{''} to the context, we propose den…
Morphological DisambiguationWord Embeddings