The DCU Discourse Parser: A Sense Classification Task

Abstract

This paper describes the discourse parsing system developed at Dublin City University for participation in the CoNLL 2015 shared task. We participated in two tasks: a connective and argument identification task and a sense classification task. This paper focuses on the latter task and especially the sense classification for implicit connectives.

Shallow Discourse Parsing Using Convolutional Neural Network

“…In CoNLL-2015, various approaches were explored to conquer the sense classification problem, which is a straightforward multi-category classification task (Okita et al, 2015;Wang and Lan, 2015;Chiarcos and Schenk, 2015;Song et al, 2015;Stepanov et al, 2015;Yoshida et al, 2015;Sun et al, 2015;Nguyen et al, 2015;Laali et al, 2015). Typical data-driven machine learning methods, like Maximum Entropy and Support Vector Machine, were adopted.…”

“…Brown cluster features, surface features and entity semantics were also effective to enhance sense classification. Additionally, paragraph embeddings were also used to determine the senses (Okita et al, 2015). In other previous work of implicit sense classification, Chen et al (2015) used word-pair features for predicting missing connectives, Zhou et al (2010) attempted to insert discourse connectives between arguments with the use of a language model, Lin et al (2009) applied various feature selection methods.…”