A Morphology-based Representation Model for LSTM-based Dependency Parsing of Agglutinative Languages
2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018, Brussels, Belçika, 31 Ekim - 01 Kasım 2018, ss.238-247, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.18653/v1/k18-2024
- Basıldığı Şehir: Brussels
- Basıldığı Ülke: Belçika
- Sayfa Sayıları: ss.238-247
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Boğaziçi Üniversitesi Adresli: Evet
Özet
We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.