We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused on dialogue act, relevance and discourse segment. In dialogue act tagging, we have implemented a transformation-based learning procedure and resulted in 70% accuracy in open test. In relevance and discourse segment tagging, we have implemented a ...

This paper describes the submitted discourse parsing system of the natural language group of Soochow University (SoNLP-DP) to the CoNLL 2015 shared task. Our System classifies discourse relations into explicit and non-explicit relations and uses a pipeline platform to conduct every subtask to form an end-toend shallow discourse parser in the Penn Discourse Treebank (PDTB). Our system is evaluat...

The corpora available for training discourse relation classifiers are annotated using a general set of discourse relations. However, for certain applications, custom discourse relations are required. Creating a new annotated corpus with a new relation taxonomy is a timeconsuming and costly process. We address this problem by proposing a semi-supervised approach to discourse relation classificat...

People with severe traumatic brain injury (TBI) often make errors on everyday tasks that compromise their safety and independence. Such errors potentially arise from the breakdown or failure of multiple cognitive processes. This study aimed to investigate cognitive deficits underlying error behavior on a home-based version of the Cooking Task (HBCT) following TBI. Participants included 45 adult...

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domainindependent neural models of discourse coherence that are capable of me...

The work presented here describes our participation in CoNLL 2015 shared task in the closed track. Here we have used a hybrid approach, where Machine Learning (ML) technique and linguistic rules are used to identify the discourse relations. We have developed this system with a view that it consistently works across all domains and all types of text corpus. We have obtained encouraging results. ...

Notwithstanding the fact that extensive studies have highlighted positive impact of flipped learning on developing learners’ skills, literature has still been silent its effect Indonesian English communicative competence. To address this issue, experimental study sheds some light significant method A total 40 learners a university in Indonesia were grouped into and non-flipped classrooms. Throu...

Japanese speakers have a choice between canonical SOV and scrambled OSV word order to express the same meaning. Although previous experiments examine the influence of one or two factors for scrambling in a controlled setting, it is not yet known what kinds of multiple effects contribute to scrambling. This study uses naturally distributed data to test the multiple effects on scrambling simultan...

To understand a discourse the reader needs to recover the relations between the discourse elements as intended by the writer Writers can and very often do help the reader along by providing explicit lexical signals of the intended discourse relations through the use of lexicalised discourse markers In this paper we present a novel approach for generating texts containing multiple embedded rheto...

To understand a discourse, the reader needs to recover the relations between the discourse elements as intended by the writer. Writers can, and very often do, help the reader along by providing explicit lexical signals of the intended discourse relations through the use of lexicalised discourse markers. In this paper we present a novel approach for generating texts containing multiple, embedded...