3,392 research outputs found
Learning Recursive Segments for Discourse Parsing
Automatically detecting discourse segments is an important preliminary step
towards full discourse parsing. Previous research on discourse segmentation
have relied on the assumption that elementary discourse units (EDUs) in a
document always form a linear sequence (i.e., they can never be nested).
Unfortunately, this assumption turns out to be too strong, for some theories of
discourse like SDRT allows for nested discourse units. In this paper, we
present a simple approach to discourse segmentation that is able to produce
nested EDUs. Our approach builds on standard multi-class classification
techniques combined with a simple repairing heuristic that enforces global
coherence. Our system was developed and evaluated on the first round of
annotations provided by the French Annodis project (an ongoing effort to create
a discourse bank for French). Cross-validated on only 47 documents (1,445
EDUs), our system achieves encouraging performance results with an F-score of
73% for finding EDUs.Comment: published at LREC 201
Introduction to the CoNLL-2001 Shared Task: Clause Identification
We describe the CoNLL-2001 shared task: dividing text into clauses. We give
background information on the data sets, present a general overview of the
systems that have taken part in the shared task and briefly discuss their
performance
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 artĂculo el primer segmentador discursivo para el euskera (EusEduSeg) implementado con heurĂsticas basadas en dependencias sintácticas y reglas lingĂĽĂsticas. Experimentos preliminares muestran resultados de más del 85 % F1 en el etiquetado de EDUs sobre el Basque RST TreeBank
Centering, Anaphora Resolution, and Discourse Structure
Centering was formulated as a model of the relationship between attentional
state, the form of referring expressions, and the coherence of an utterance
within a discourse segment (Grosz, Joshi and Weinstein, 1986; Grosz, Joshi and
Weinstein, 1995). In this chapter, I argue that the restriction of centering to
operating within a discourse segment should be abandoned in order to integrate
centering with a model of global discourse structure. The within-segment
restriction causes three problems. The first problem is that centers are often
continued over discourse segment boundaries with pronominal referring
expressions whose form is identical to those that occur within a discourse
segment. The second problem is that recent work has shown that listeners
perceive segment boundaries at various levels of granularity. If centering
models a universal processing phenomenon, it is implausible that each listener
is using a different centering algorithm.The third issue is that even for
utterances within a discourse segment, there are strong contrasts between
utterances whose adjacent utterance within a segment is hierarchically recent
and those whose adjacent utterance within a segment is linearly recent. This
chapter argues that these problems can be eliminated by replacing Grosz and
Sidner's stack model of attentional state with an alternate model, the cache
model. I show how the cache model is easily integrated with the centering
algorithm, and provide several types of data from naturally occurring
discourses that support the proposed integrated model. Future work should
provide additional support for these claims with an examination of a larger
corpus of naturally occurring discourses.Comment: 35 pages, uses elsart12, lingmacros, named, psfi
Splitting Arabic Texts into Elementary Discourse Units
International audienceIn this article, we propose the first work that investigates the feasibility of Arabic discourse segmentation into elementary discourse units within the segmented discourse representation theory framework. We first describe our annotation scheme that defines a set of principles to guide the segmentation process. Two corpora have been annotated according to this scheme: elementary school textbooks and newspaper documents extracted from the syntactically annotated Arabic Treebank. Then, we propose a multiclass supervised learning approach that predicts nested units. Our approach uses a combination of punctuation, morphological, lexical, and shallow syntactic features. We investigate how each feature contributes to the learning process. We show that an extensive morphological analysis is crucial to achieve good results in both corpora. In addition, we show that adding chunks does not boost the performance of our system
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