Fast Rhetorical Structure Theory Discourse Parsing

In recent years, There has been a variety of research on discourse parsing, particularly RST discourse parsing. Most of the recent work on RST parsing has focused on implementing new types of features or learning algorithms in order to improve accuracy, with relatively little focus on efficiency, robustness, or practical use. Also, most implementations are not widely available. Here, we describe an RST segmentation and parsing system that adapts models and feature sets from various previous work, as described below. Its accuracy is near state-of-the-art, and it was developed to be fast, robust, and practical. For example, it can process short documents such as news articles or essays in less than a second

Adaptation of discourse parsing models for the portuguese language

Discourse parsing in Portuguese has two critical limitations. The first is that the task has been explored using only symbolic approaches, i.e., using manually extracted lexical patterns. The second is related to the domain of the lexical patterns, which were extracted through the analysis of a corpus of academic texts, generating many domain-specific patterns. For English, many approaches have been explored using machine learning with features based on a prominent lexicon-syntax notion of dominance sets. In this paper, two works were adapted to Portuguese, improving the results, outperforming the baselines and previous works for Portuguese, considering the task of rhetorical relation identification.São Paulo Research Foundation (FAPESP) (grant 2014/11632-0)Natural Sciences and Engineering Research Council of CanadaUniversity of Toront

Because Syntax does Matter: Improving Predicate-Argument Structures Parsing Using Syntactic Features

International audienceParsing full-fledged predicate-argument structures in a deep syntax framework requires graphs to be predicted. Using the DeepBank (Flickinger et al., 2012) and the Predicate-Argument Structure treebank (Miyao and Tsujii, 2005) as a test field, we show how transition-based parsers, extended to handle connected graphs, benefit from the use of topologically different syntactic features such as dependencies, tree fragments, spines or syntactic paths, bringing a much needed context to the parsing models, improving notably over long distance dependencies and elided coordinate structures. By confirming this positive impact on an accurate 2nd-order graph-based parser (Martins and Almeida, 2014), we establish a new state-of-the-art on these data sets

EusDisParser: improving an under-resourced discourse parser with cross-lingual data

International audienceDevelopment of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first parser based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results

Constrained decoding for text-level discourse parsing

International audienceThis paper presents a novel approach to document-based discourse analysis by performing a global A* search over the space of possible structures while optimizing a global criterion over the set of potential coherence relations. Existing approaches to discourse analysis have so far relied on greedy search strategies or restricted themselves to sentence-level discourse parsing. Another advantage of our approach, over other global alternatives (like Maximum Spanning Tree decoding algorithms), is its flexibility in being able to integrate constraints (including linguistically motivated ones like the Right Frontier Constraint). Finally, our paper provides the first discourse parsing system for French; our evaluation is carried out on the Annodis corpus. While using a lot less training data than earlier approaches than previous work on English, our system manages to achieve state-of-the-art results, with F1-scores of 66.2 and 46.8 when compared to unlabeled and labeled reference structures

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