Joint learning of morphological segmentation, morpheme tagging, part-of-speech tagging, and dependency parsing

Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Turkey

Approval Date: 2020

Student: Hüseyin. Aleçakır



In agglutinating languages, there is a strong relationship between morphology and syntax. Inflectional and derivational suffixes have a significant role while determining the syntactic role of the word in the sentence. This connection enables the joint learning of morphology and syntax. Apart from that, the complex morphology poses a sparsity problem. In this respect, morphological analysis and segmentation are vital for various natural language processing applications. All of these have provided the primary motivation to develop a joint learning model for morphological segmentation, morphological tagging, part-of-speech (POS) tagging, and dependency parsing. The proposed model consists of a multi-layered neural network structure. In each level, there is a bidirectional long-short memory unit (BiLSTM) to encode sequential information. Additionally, attention networks are used to compute soft alignment between encoder-decoder states in the morphological tagging component. Finally, the obtained results from each layer of the network are compared with other works from the literature. The results are very competitive on Universal Dependencies (UD) dataset.