A PDTB-Styled End-to-End Discourse Parser

We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table

Cross-lingual RST Discourse Parsing

Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page

Improving discourse structure identification

Rhetorical Structure Theory (Mann et al. 1988), a popular approach for analyzing discourse coherence, suggests that coherent text can be placed into a hierarchical organization of clauses. Identification of a text’s rhetorical structure through automatic discourse analysis is a crucial element for many of today’s Natural Language Processing tasks, but no sufficient tool is available. The current state-of -the-art discourse parser, SPADE (Soricut et al. 2003), is limited to parsing discourse within a single sentence. HILDA (Hernault et al. 2010) extends the parsing abilities of SPADE to the document level, but with a decrease in performance. This study achieved document-level discourse parsing without sacrificing performance. Provided text was already segmented into elementary discourse units, the task of discourse parsing was separated into three steps: structuring, nuclearity labeling, and relation labeling. An algorithm was developed for classifying relation existence, nuclearity, and relation label that improved upon previous methods. New features were explored for all three steps to maintain state-of-the-art performance when parsing at the document-level

Learning Explicit and Implicit Arabic Discourse Relations.

We propose in this paper a supervised learning approach to identify discourse relations in Arabic texts. To our knowledge, this work represents the first attempt to focus on both explicit and implicit relations that link adjacent as well as non adjacent Elementary Discourse Units (EDUs) within the Segmented Discourse Representation Theory (SDRT). We use the Discourse Arabic Treebank corpus (D-ATB) which is composed of newspaper documents extracted from the syntactically annotated Arabic Treebank v3.2 part3 where each document is associated with complete discourse graph according to the cognitive principles of SDRT. Our list of discourse relations is composed of a three-level hierarchy of 24 relations grouped into 4 top-level classes. To automatically learn them, we use state of the art features whose efficiency has been empirically proved. We investigate how each feature contributes to the learning process. We report our experiments on identifying fine-grained discourse relations, mid-level classes and also top-level classes. We compare our approach with three baselines that are based on the most frequent relation, discourse connectives and the features used by Al-Saif and Markert (2011). Our results are very encouraging and outperform all the baselines with an F-score of 78.1% and an accuracy of 80.6%

Discourse Relations and Connectives in Higher Text Structure

The present article investigates possibilities and limits of local (shallow) analysis of discourse coherence with respect to the phenomena of global coherence and higher composition of texts. We study corpora annotated with local discourse relations in Czech and partly in English to try and find clues in the local annotation indicating a higher discourse structure. First, we classify patterns of subsequent or overlapping pairs of local relations, and hierarchies formed by nested local relations. Special attention is then given to relations crossing paragraph boundaries and their semantic types, and to paragraph-initial discourse connectives. In the third part, we examine situations in which annotators incline to marking a large argument (larger than one sentence) of a discourse relation even with a minimality principle annotation rule in place. Our analyses bring (i) new linguistic insights regarding coherence signals in local and higher contexts, e.g. detection and description of hierarchies of local discourse relations up to 5 levels in Czech and English, description of distribution differences in semantic types in cross-paragraph and other settings, identification of Czech connectives only typical for higher structures, or the detection of prevalence of large left-sided arguments in locally annotated data; (ii) as another type of contribution, some new reflections on methodologies of the approaches under scrutiny

A corpus analysis of discourse relations for Natural Language Generation

We are developing a Natural Language Generation (NLG) system that generates texts tailored for the reading ability of individual readers. As part of building the system, GIRL (Generator for Individual Reading Levels), we carried out an analysis of the RST Discourse Treebank Corpus to find out how human writers linguistically realise discourse relations. The goal of the analysis was (a) to create a model of the choices that need to be made when realising discourse relations, and (b) to understand how these choices were typically made for “normal” readers, for a variety of discourse relations. We present our results for discourse relations: concession, condition, elaboration additional, evaluation, example, reason and restatement. We discuss the results and how they were used in GIRL

EusEduSeg: Un Segmentador Discursivo para el Euskera Basado en Dependencias

We present the first discursive segmenter for Basque implemented by heuristics based on syntactic dependencies and linguistic rules. Preliminary experiments show F1 values of more than 85% in automatic EDU segmentation for Basque.Presentamos en este artículo el primer segmentador discursivo para el euskera (EusEduSeg) implementado con heurísticas basadas en dependencias sintácticas y reglas lingüísticas. Experimentos preliminares muestran resultados de más del 85 % F1 en el etiquetado de EDUs sobre el Basque RST TreeBank

CRPC-DB – A Discourse Bank for Portuguese

info:eu-repo/semantics/publishedVersio

Linguistic Tests for Discourse Relations

Discourse structure and discourse relations are an important ingredient in systems for the analysis of text that go beyond the boundary of single clauses. Discourse relations often indicate important additional information about the connection between two clauses, such as causality, and are widely believed to have an influence on aspects of reference resolution.In this article, we first present the general design choices that are to be made in the design of an annotation scheme for discourse structure and discourse relations. In a second part, we present the scheme used in our annotation of selected articles from the TüBa-D/Z treebank of German (Telljohann et al., 2009). The scheme used in the annotation is theory-neutral, but informed by more detailed linguistic knowledge in the way of linguistic tests that can help disambiguate between several plausible relations