LiveTree

Proceedings of the 2004 ACL Workshop on Discourse Annotation - DiscAnnotation '04, 2004

In this paper, we describe how the LIDAS System (Linguistic Discourse Analysis System), a discourse parser built as an implementation of the Unified Linguistic Discourse Model (U-LDM) uses information from sentential syntax and semantics along with lexical semantic information to build the Open Right Discourse Parse Tree (DPT) that serves as a representation of the structure of the discourse Thione 2004a,b). More specifically, we discuss how discourse segmentation, sentence-level discourse parsing, and text-level discourse parsing depend on the relationship between sentential syntax and discourse. Specific discourse rules that use syntactic information are used to identify possible attachment points and attachment relations for each Basic Discourse Unit to the DPT.

2008

In this paper, we investigate which information is useful for the detection of rhetorical (RST) relations between (Multi-) Sentential Discourse Units ((M-)SDUs)-text spans consisting of one or more sentences-within the same paragraph. In order to do so, we simplified the task of discourse parsing to a decision problem in which we decided whether an (M-)SDU is either rhetorically related to a preceding or a following (M-)SDU. Employing the RST Treebank , we offered this choice to machine learning algorithms together with syntactic, lexical, referential, discourse and surface features. Next, the features were ranked on the basis of (1) models established by the classification algorithms and (2) feature selection metrics. Highly ranked features that predict the presence of a rhetorical relation are syntactic similarity, word overlap, word similarity, continuous punctuation and many reference features. Other features are used to introduce new topics or arguments: time references, proper nouns, definite articles and the word further.

Zeitschrift fur Sprachwissenschaft, 2007

Predicting discourse structure on naturally occurring texts and dialogs is challenging and computationally intensive. Attempts to construct hand-built systems have run into problems both in how to specify the required knowledge and how to perform the necessary ...

2004

This paper presents a system for automatically generating discourse structures from written text. The system is divided into two levels: sentence-level and text-level. The sentence-level discourse parser uses syntactic information and cue phrases to segment sentences into elementary discourse units and to generate discourse structures of sentences. At the text-level, constraints about textual adjacency and textual organization are integrated in a beam search in order to generate best discourse structures. The experiments were done with documents from the RST Discourse Treebank. It shows promising results in a reasonable search space compared to the discourse trees generated by human analysts.

2012

Abstract In this paper, we develop an RST-style textlevel discourse parser, based on the HILDA discourse parser (Hernault et al., 2010b). We significantly improve its tree-building step by incorporating our own rich linguistic features. We also analyze the difficulty of extending traditional sentence-level discourse parsing to text-level parsing by comparing discourseparsing performance under different discourse conditions.

Computational Intelligence, 2014

Discourse parsing has become an inevitable task to process information in the natural language processing arena. Parsing complex discourse structures beyond the sentence level is a significant challenge. This article proposes a discourse parser that constructs rhetorical structure (RS) trees to identify such complex discourse structures. Unlike previous parsers that construct RS trees using lexical features, syntactic features and cue phrases, the proposed discourse parser constructs RS trees using high-level semantic features inherited from the Universal Networking Language (UNL). The UNL also adds a language-independent quality to the parser, because the UNL represents texts in a language-independent manner. The parser uses a naive Bayes probabilistic classifier to label discourse relations. It has been tested using 500 Tamil-language documents and the Rhetorical Structure Theory Discourse Treebank, which comprises 21 English-language documents. The performance of the naive Bayes classifier has been compared with that of the support vector machine (SVM) classifier, which has been used in the earlier approaches to build a discourse parser. It is seen that the naive Bayes probabilistic classifier is better suited for discourse relation labeling when compared with the SVM classifier, in terms of training time, testing time, and accuracy.

Manuscript [fix this], 2003

Large scale annotated corpora have played a critical role in speech and natural language research. However, while existing annotated corpora such as the Penn Treebank have been highly successful at the sentence-level, we also need large-scale annotated resources that reliably encode key aspects of discourse. In this paper, we detail (1) our plans for building the Penn Discourse Treebank (PDTB),(2) our preliminary annotation work, and (3) the results to date of our efforts. Annotation in the PDTB will focus on coherence ...

The paper presents a new statistical method, for parsing discourse. A parse of discourse is defined as a set of semantic dependencies among sentences that make up the discourse. A collection of news articles from a Japanese economics daily are manually marked for dependency and used as a training/testing corpus. We use a C4.5 decision tree method to develop a model of sentential dependencies. However, rather than to use class decisions made by C4.5, we exploit information on class distributions to rank possible dependencies among sentences according to their probabilistic strength and take a parse to be a set of highest ranking dependencies. We also study effects of features such as clue words, distance and similarity on the performance of the discourse parser. Experiments have found that the method performs reasonably well on diverse text types, scoring an accuracy rate of over 60%.

Proceedings of the CoNLL-16 shared task, 2016

This paper describes our system in the CoNLL-2016 shared task. Our system takes a piece of newswire text as input and returns the discourse relations. In our system we use a pipeline to conduct each subtask. Our system is evaluated on the CoNLL-2016 Shared Task closed track and obtains 0.1515 in F1 measurement, especially the part of detecting connectives, which achieves 0.9838 on blind test set.

Lecture Notes in Computer Science, 2000

We present a discourse parsing architecture based on an incremental approach and aimed at building a rhetorical structure of a free text. Vein expressions computed on the developing structure help to restrict the domains of referential accessibility on which resolution of anaphora is performed. The parsing process is guided by cohesion and coherence constraints. 2 Veins Theory Veins Theory [3] uses a representation of the discourse structure as a binary tree, close to the representation in Rhetorical Structure Theory (RST) [8]. Terminal nodes in the tree represent discourse units and non-terminal nodes represent continuous text spans and are labelled with rhetorical relations. Usually a label uniquely identifies a