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May 2002
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72 Reads
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1 Citation
We show that discourse structure need not bear the full burden of conveying discourse relations by showing that many of them can be explained nonstructurally in terms of the grounding of anaphoric presuppositions (Van der Sandt, 1992). This simplifies discourse structure, while still allowing the realisation of a full range of discourse relations. This is achieved using the same semantic machinery used in deriving clause-level semantics.
May 1999
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57 Reads
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21 Citations
We show that discourse structure need not bear the full burden of conveying discourse relations by showing that many of them can be explained nonstructurally in terms of the grounding of anaphoric presuppositions (Van der Sandt, 1992). This simplifies discourse structure, while still allowing the realisation of a full range of discourse relations. This is achieved using the same semantic machinery used in deriving clause-level semantics. 1 Introduction Research on discourse structure has, by and large, attempted to associate all meaningful relations between propositions with structural connections between discourse clauses (syntactic clauses or structures composed of them). Recognising that this could mean multiple structural connections between clauses, Rhetorical Structure Theory (Mann and Thompson, 1988) simply stipulates that only a single relation may hold. Moore and Pollack (1992) argue that both informational (semantic) and intentional relations can hold between clauses simul...
... This work focuses on the task of implicit discourse relation recognition (IDRR), aiming to identify the discourse relations (i.e., cause, contrast) between adjacent text spans in documents. IDRR is a fundamental problem in discourse analysis (Knott, 2014;Webber et al., 1999) with important applications on question answering (Liakata et al., 2013;Jansen et al., 2014) and text summarization (Gerani et al., 2014;Yoshida et al., 2014), to name a few. Due it its importance, IDRR is being studied actively in the literature, leading to the recent advances for this problem based on deep learning Qin et al., 2016;Lan et al., 2017;Dai and Huang, 2018). ...
May 1999
