Constructing the CODA corpus: A parallel corpus ofmonologues and expository dialogues

We describe the construction of the CODA corpus, a parallel corpus of monologues and expository dialogues. The dialogue part of the corpus consists of expository, i.e., information-delivering rather than dramatic, dialogues written by several acclaimed authors. The monologue part of the corpus is a paraphrase in monologue form of these dialogues by a human annotator. The corpus was constructed as a resource for extracting rules for automated generation of dialogue from monologue. Using authored dialogues allows us to analyse the techniques used by accomplished writers for presenting information in the form of dialogue. The dialogues are annotated with dialogue acts and the monologues with rhetorical structure. We developed annotation and translation guidelines together with a custom-developed tool for carrying out translation, alignment and annotation

Generating ellipsis using discourse structures

This article describes an effort to generate elliptic sentences, using Dependency Trees connected by Discourse Relations as input. We contend that the process of syntactic aggregation should be performed in the Surface Realization stage of the language generation process, and that Dependency Trees with Rhetorical Relations are excellent input for a generation system that has to generate ellipsis. We also propose a taxonomy of the most common Dutch cue words, grouped according to the kind of discourse relations they signal

Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method's results proved the high accuracy of aspect detection when applied to the gold standard dataset

Language choice models for microplanning and readability

This paper describes the construction of language choice models for the microplanning of discourse relations in a Natural Language Generation system that attempts to generate appropriate texts for users with varying levels of literacy. The models consist of constraint satisfaction problem graphs that have been derived from the results of a corpus analysis. The corpus that the models are based on was written for good readers. We adapted the models for poor readers by allowing certain constraints to be tightened, based on psycholinguistic evidence. We describe how the design of microplanner is evolving. We discuss the compromises involved in generating more readable textual output and implications of our design for NLG architectures. Finally we describe plans for future work

A short survey of discourse representation models

With the advancement of technology and the wide adoption of ontologies as knowledge representation formats, in the last decade, a handful of models were proposed for the externalization of the rhetoric and argumentation captured within scientific publications. Conceptually, most of these models share a similar representation form of the scientific publication, i.e. as a series of interconnected elementary knowledge items. The main differences are given by the terminology used, the types of rhetorical and/or argumentation relations connecting the knowledge items and the foundational theories supporting these relations. This paper analyzes the state of the art and provides a concise comparative overview of the five most prominent discourse representation models, with the goal of sketching an unified model for discourse representation

Discourse relations and conjoined VPs: automated sense recognition

Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (inter-sentential) and within sentences (intra-sentential), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification system for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser