May 13, 2020

Berkay Furkan Önder, M.S. 2020

Contact info: Email, GitHub.
M.S Thesis: Effect Of Contextual Embeddings on Graph-Based Dependency Parsing, Koç University, Department of Computer Engineering. May 2020. (PDF, Presentation).
Publications: CoNLL18 and CoNLL17

Thesis Abstract:
I demonstrate the effect of contextual embeddings on transition and graph-based parsing methods and test our contribution, the structured meta biaffine decoder, using various graph-based parsing algorithms.
As Koc University Graph-Based parsing team, we implemented a graph-based dependency parsing model in order to perform syntactic and semantic analysis of given sentences. Our neural graph-based parser consists of two main parts, which are encoder and decoder. The encoder forms continuous feature vectors from provided sentences for the neural graph-based parser to process the texts properly, whereas the decoder produces the parse tree from the output of the neural parser, by first producing a graph representation of the output. We participated in CoNLL 2018 Shared Task with the parsing model we created, and had the opportunity to run our model on 61 different data sets formed with texts in 41 different languages. We took advantage of natural language processing and deep learning techniques, including graph-based dependency parsing algorithms.

No comments: