A Study to Improve the Efficiency of a Discourse Parsing System

Automatic Generation of Multi-Modal Dialogue from Text Based on Discourse Structure Analysis

“…In order to generate RST trees automatically, we employ the Discourse Analysis System (DAS) [14], a stateof-the-art syntactical parsing system based on cue phrases and various textual coherence structures. An alternative machine learning based approach relying on an ensemble of support vector classifiers is presented in [25].…”

Generating discourse structures for written texts

“…To posit relation names, we combine several factors, including syntactic information, cue phrases, NP-cues, VP-cues 2 , and cohesive d evices (e.g., synonyms and hyponyms derived from WordNet) (Le and Abeysinghe 2003). With the presented method of constructing sentential discourse trees based on syntactic information and cue phrases, combinatorial explosions can be prevented and still get accurate analyses.…”

“…To generate discourse structures at the text-level, the constraints of textual organization and textual adjacency are used to initiate all possible connections among text spans. Then, all possible discourse relations between text spans are posited based on cue phrases, NP-cues, VP-cues and other cohesive devices (Le and Abeysinghe 2003). Based on this relation set, the system should generate the best discourse trees, each of which covers the entire text.…”

T2D: Generating Dialogues Between Virtual Agents Automatically from Text

“…Analyzer: A component that analyses text in terms of Rhetorical Structure Theory (RST, [10]). Currently, it consists of the DAS Discourse Analyzing System [9] which builds RST structures (but without identifying nuclei and satellites), and a nucleus/satellite Identification Module; 2. Mapper: Module that maps RST structures to DialogueNet structures (these are a specific subclass of RST structures that represent dialogue); 3.…”