Computational Models of Discourse: Discourse Parsing Caroline Sporleder Universit¨ at des Saarlandes Sommersemester 2009 24.06.2009 Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Roadmap Four interdependent aspects/dimensions of discourse structure: Linguistic Structure: linguistic manifestation of discourse structure, e.g., lexical cohesion, discourse connectives/cue words, intonation, gesture, referring expressions etc. Intentional Structure: each discourse segment fulfils a purpose (why does a speaker/write make a given utterance in a given form?) Informational Structure: how do the different segments of a discourse relate to each other (which discourse relations hold)? Focus/Attentional Structure: which entities are salient at a given point in discourse? Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Roadmap We’ve addressed sofar . . . Linguistic Structure: lexical chains word distributions for text segmentation from a generation perspective: generating referring expressions co-reference resolution Focus/Attentional Structure: Centering Theory Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Now . . . Informational Structure (and a bit Intentional Structure) temporal ordering (last week) Rhetorical Structure Theory discourse parsing Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Modelling Discourse Structure Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Modelling Discourse Structure Various Discourse Theories: Discourse Structure Theory (DST) (Grosz & Sidner, 1986) Rhetorical Structure Theory (RST) (Mann & Thompson, 1987) Discourse Representation Theory (DRT) (Kamp & Reyle, 1993) Segmented Discourse Representation Theory (SDRT) (Asher & Lascarides 2003) What these discourse theories share: model how different segments of a discourse relate to each other (informational structure) assume hierarchical discourse structure more or less pre-defined inventory of discourse relations Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Modelling Discourse Structure Various Discourse Theories: Discourse Structure Theory (DST) (Grosz & Sidner, 1986) Rhetorical Structure Theory (RST) (Mann & Thompson, 1987) Discourse Representation Theory (DRT) (Kamp & Reyle, 1993) Segmented Discourse Representation Theory (SDRT) (Asher & Lascarides 2003) What these discourse theories share: model how different segments of a discourse relate to each other (informational structure) assume hierarchical discourse structure more or less pre-defined inventory of discourse relations Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Rhetorical Structure Theory (RST) (Mann & Thompson, 1987) Origins: originally developed for text generation aim: framework for structural description of the meaning of a given text RST-Analysis: what was the intention of the writer (according to the interpretation of the analyst)? exact intention of the writer is not always clear ⇒ possibility of several analyses for a given text RST website: http://www.sfu.ca/rst/ Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Rhetorical Structure Theory (RST) (Mann & Thompson, 1987) Elements of RST: elementary discourse units (EDUs) (usually clauses) edus and higher-level discourse segments linked by a pre-defined set of 24-30 rhetorical relations discourse segments function as nucleus (N - more important) and satellite (S - less important) most relations are binary and mono-nuclear: N+S or S+N some multi-nuclear (e.g. contrast ) and non-binary relations (e.g. joint ) Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. [ Nora sleeps a lot ] N [ because she is ill. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. [ Nora sleeps a lot ] N [ because she is ill. ] S Tom is going to the theatre, not to the cinema. Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. [ Nora sleeps a lot ] N [ because she is ill. ] S Tom is going to the theatre, not to the cinema. [ Tom is going to the theatre, ] N [ not to the cinema. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. [ Nora sleeps a lot ] N [ because she is ill. ] S Tom is going to the theatre, not to the cinema. [ Tom is going to the theatre, ] N [ not to the cinema. ] S Today the wheather was nice, it didn’t rain. Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Nucleus vs. Satellite Nora sleeps a lot because she is ill. [ Nora sleeps a lot ] N [ because she is ill. ] S Tom is going to the theatre, not to the cinema. [ Tom is going to the theatre, ] N [ not to the cinema. ] S Today the wheather was nice, it didn’t rain. [ Today the wheather was nice, ] N [ it didn’t rain. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S ⇒ Explanation Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S ⇒ Explanation [ Tom is going to the theatre, ] N [ not to the cinema. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S ⇒ Explanation [ Tom is going to the theatre, ] N [ not to the cinema. ] S ⇒ Antithesis Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S ⇒ Explanation [ Tom is going to the theatre, ] N [ not to the cinema. ] S ⇒ Antithesis [ Today the wheather was nice, ] N [ it didn’t rain. ] S Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Example: Rhetorical Relations [ Nora sleeps a lot ] N [ because she is ill. ] S ⇒ Explanation [ Tom is going to the theatre, ] N [ not to the cinema. ] S ⇒ Antithesis [ Today the wheather was nice, ] N [ it didn’t rain. ] S ⇒ Elaboration Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Definitions of Discourse Relations Example: Evidence [ This tax calculation software really works. ] N [ I entered all the figures from my tax return and got a result which agreed with my hand calculations to the penny. ] S relation name: evidence constraints on N: Reader (R) might not believe N to a degree satisfactory to Writer (W) constraints on S: R believes S or finds it credible constraints on N+S: R’s comprehending S increases R’s belief of N effect: R’s belief of N is increased locus of effect: N Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Definitions of Discourse Relations Antithesis (mono-nuclear) [ Peter went to the theatre, ] N [ not the cinema ] S . constraints on N: W has positive attitude to N constraints on N+S: situations are contrasted effect: R’s positive attitude to N is enhanced Contrast (multi-nuclear) [ Peter likes chocolate, ] N [ Mary likes crisps. ] N constraints: situations described by nuclei are contrasted, both nuclei have equal weight effect: R understands the similarities and differences between both situations Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
Simple Example Result Explanation Contrast Peter failed because he didn’t He had to spend while his friends the exam study hard enough. the holidays preparing enjoyed themselves for the re−sit at the beach Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
A More Complex Example Raw Text Famington Police had to help control traffic recently when hundreds of people lined up to be among the first applying for jobs at the yet-to-open Mariott Hotel. The hotel’s “help-wanted” announcement for 300 openings was a rare opportunity for many unemployed. The people waiting in line carried a message, a refutation, of the claims that the jobless could be employed if only they showed enough ambition. Every rule has exceptions but the tragic and too common tableaux of hundreds or even thousands of people snake-lining up for any task with a paycheck illustrates a lack of jobs, not laziness. Caroline Sporleder csporled@coli.uni-sb.de Computational Models of Discourse
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