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.
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