Temporal Structure of Discourse (original) (raw)

Algorithms for analysing the temporal structure of discourse

Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics -, 1995

We describe a method for analysing the temporal structure of a discourse which takes into account the effects of tense, aspect, temporal adverbials and rhetorical structure and which minimises unnecessary ambiguity in the temporal structure• It is part of a discourse grammar implemented in Carpenter's ALE formalism. The method for building up the temporal structure of the discourse combines constraints and preferences: we use constraints to reduce the number of possible structures, exploiting the HPSG type hierarchy and unification for this purpose; and we apply preferences to choose between the remaining options using a temporal centering mechanism• We end by recommending that an underspecified representation of the structure using these techniques be used to avoid generating the temporal/rhetorical structure until higher-level information can be used to disambiguate. the temporal component were to yield a detailed representation of the temporal structure of the discourse, taking into account the effect of tense, aspect and temporal expressions while at the same time minimising unnecessary ambiguity in the temporal structure. The method combines a constraint-based approach with an approach based on preferences: we exploit the HPSG type hierarchy and unification to arrive at a temporal structure using constraints placed on that structure by tense, aspect, rhetorical structure and temporal expressions, and we use the temporal centering preferences described by (Kameyama et al., 1993; Poesio, 1994) to rate the possibilities for temporal structure and choose' the best among them. The starting point for this work was Scha and Polanyi's discourse grammar (Scha Polanyi 1988; Priist et al 1994). For the implementation we extended the HPSG grammar (Pollard and Sag, 1994) which Gerald Penn and Bob Carpenter first encoded in ALE (Carpenter, 1993). This paper will focus on our temporal processing algorithm, and in particular on our analysis of narrative progression, rhetorical structure, perfects and temporal expressions•

A syntactic and lexical-based discourse segmenter

Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009

We present a syntactic and lexically based discourse segmenter (SLSeg) that is designed to avoid the common problem of over-segmenting text. Segmentation is the first step in a discourse parser, a system that constructs discourse trees from elementary discourse units. We compare SLSeg to a probabilistic segmenter, showing that a conservative approach increases precision at the expense of recall, while retaining a high F-score across both formal and informal texts.

Automated Discourse Segmentation by Syntactic Information and Cue Phrases

This paper presents an approach to automatic segmentation of English written text into Elementary Discourse Units (EDUs) 1 using syntactic information and cue phrases. The system takes documents with syntactic information as the input and generates EDUs as well as their nucleus/satellite roles. The experiment shows that this approach can give promising result in comparison with existing research in discourse segmentation.

Defining discourse formulae: computational approach

In this paper, we address the problem of automatic extraction of discourse formulae. By discourse formulae (DF) we mean a special type of constructions at the discourse level, which have a fixed form and serve as a typical response in the dialogue. Unlike traditional constructions [4, 5, 6], they do not contain variables within the sequence; their slots can be found in the left-hand or right-hand statements of the speech act. We have developed the system that extracts DF from drama texts. We have compared token-based and clause- based approaches and found the latter performing better. The clause-based model involves a uniform weight vote of four classifiers and currently shows the precision of 0.30 and the recall of 0.73 (F1-score 0.42).The created module was used to extract a list of DF from 420 drama texts of XIX-XXI centuries [1, 7]. The final list contains 3000 DF, 1800 of which are unique. Further development of the project includes enhancing the module by extracting left conte...

The optimization of discourse anaphora

Linguistics and Philosophy, 2004

In this paper the Centering model of anaphora resolution and discourse coherence Weinstein, 1983, 1995) is reformulated in terms of Optimality Theory (ot) . One version of the reformulated model is proven to be descriptively equivalent to an earlier algorithmic statement of Centering due to . However, the new model is stated declaratively, and makes clearer the status of the various constraints used in the theory. In the second part of the paper, the model is extended, demonstrating the advantages of the ot reformulation, and capturing formally ideas originally described by Grosz, Joshi and Weinstein. Three new applications of the extended ot Centering model are described: generation of linguistic forms from meanings, the evaluation and optimization of extended texts, and the interpretation of accented pronouns.

A Linguistically-based Approach to Discourse Relations Recognition

Proceedings of the 4th International Workshop on Natural Language Processing and Cognitive Science, 2007

We present an unsupervised linguistically-based approach to discourse relations recognition, which uses publicly available resources like manually annotated corpora (Discourse Graphbank, Penn Discourse Treebank, RST-DT), as well as empirically derived data from "causally" annotated lexica like LCS, to produce a rule-based algorithm. In our approach we use the subdivision of Discourse Relations into four subsets-CONTRAST, CAUSE, CONDITION , ELABORATION, proposed by [7] in their paper, where they report results obtained with a machine-learning approach from a similar experiment, against which we compare our results. Our approach is fully symbolic and is partially derived from the system called GETARUNS, for text understanding, adapted to a specific task: recognition of Causality Relations in free text. We show that in order to achieve better accuracy, both in the general task and in the specific one, semantic information needs to be used besides syntactic structural information. Our approach outperforms results reported in previous papers [9].

Easily Identifiable Discourse Relations

We present a corpus study of local dis- course relations based on the Penn Dis- course Tree Bank, a large manually anno- tated corpus of explicitly or implicitly re- alized relations. We show that while there is a large degree of ambiguity in temporal explicit discourse connectives, overall con- nectives are mostly unambiguous and al- low high-accuracy prediction of discourse relation type. We achieve 93.09% accu- racy in classifying the explicit relations and 74.74% accuracy overall. In addition, we show that some pairs of relations oc- cur together in text more often than ex- pected by chance. This finding suggests that global sequence classification of the relations in text can lead to better results, especially for implicit relations.