Work

Stem-based PoS tagging for agglutinative languages

Year: 2017

Type: conference-paper

Abstract: Words are made up of morphemes being glued together in agglutinative languages. This makes it difficult to perform part-of-speech tagging for these languages due to sparsity. In this paper, we present two Hidden Markov Model based Bayesian PoS tagging models for agglutinative languages. Our first model is word-based and the second model is stem-based where the stems of the words are obtained from other two unsupervised stemmers: HPS stemmer and Morfessor FlatCat. The results show that stemming improves the accuracy in PoS tagging. We present the results for Turkish as an agglutinative language and English as a morphologically poor language.

Source: Signal Processing and Communications Applications Conference (SIU)

DOI: 10.1109/siu.2017.7960386

Authors: Necva Bolucu, Burcu Can

Institution: Hacettepe University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2728343294

FWCI: 0.374

Cites: 16

Cited by: 7

Related to: 10