Learning word vectors from character level is an effective method to improve word embeddings for morphologically rich languages. However, most of these techniques have been applied to languages that are inflectional and written in Roman alphabets. In this paper, we investigate languages that are agglutinative and represented by non-alphabetic scripts, choosing Korean as a case study. We present a grapheme-level coding procedure for neural word embedding that utilizes word-internal features that are composed of syllable characters (Character CNN). Observing that our grapheme-level model is more capable of representing functional and semantic similarities, grouping allomorphs, and disambiguating homographs than syllable-level and word-level models, we recognize the importance of knowledge on the morphological typology and diversity of writing systems.
@InProceedings{PARK18.133, author = {Suzi Park and Hyopil Shin}, title = "{Grapheme-level Awareness in Word Embeddings for Morphologically Rich Languages}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper