@phdthesis{FengPhD,
  author = "Vanessa Wei Feng",
  title = "RST-Style Discourse Parsing and Its Applications in Discourse Analysis",
  school = "Department of Computer Science, University of Toronto",
  year = "2015",
  abstract = "Discourse parsing is the task of identifying the
                  relatedness and the particular discourse relations
                  among various discourse units in a text. In
                  particular, among various theoretical frameworks of
                  discourse parsing, I am interested in Rhetorical
                  Structure Theory (RST). I hypothesize that, given
                  its ultimate success, discourse parsing can provide
                  a general solution for use in many downstream
                  applications.  <P> This thesis is composed of two
                  major parts. First, I overview my work on discourse
                  segmentation and discourse tree-building, which are
                  the two primary components of RST-style discourse
                  parsing. Evaluated on the RST Discourse Treebank
                  (RST-DT), both of my discourse segmenter and
                  tree-builder achieve the state-of-the-art
                  performance.  <P> Later, I discuss the application
                  of discourse relations to some specific tasks in the
                  analysis of discourse, including the evaluation of
                  coherence, the identification of authorship, and the
                  detection of deception. In particular, I propose to
                  use a set of application-neutral features, which are
                  derived from the discourse relations extracted by my
                  discourse parser, and compare the performance of
                  these application-neutral features against the
                  classic application-specific approaches to each of
                  these tasks. On the first two tasks, experimental
                  results show that discourse relation features by
                  themselves often perform as well as those classic
                  application-specific features, and the combination
                  of these two kinds of features usually yields
                  further improvement. These results provide strong
                  evidence for my hypothesis that discourse parsing is
                  able to provide a general solution for the analysis
                  of discourse. However, we failed to observe a
                  similar effectiveness of discourse parsing on the
                  third task, the detection of deception. I postulate
                  that this might be due to several confounding
                  factors of the task itself.", 
  download = "http://ftp.cs.toronto.edu/pub/gh/Feng-thesis-2015.pdf"
}

{"_id":"NoqB5YtMqtb67MbMq","bibbaseid":"feng-rststylediscourseparsinganditsapplicationsindiscourseanalysis-2015","downloads":0,"creationDate":"2016-10-19T19:03:53.835Z","title":"RST-Style Discourse Parsing and Its Applications in Discourse Analysis","author_short":["Feng, V. W."],"year":2015,"bibtype":"phdthesis","biburl":"http://www.cs.toronto.edu/compling/all_bib.bib","bibdata":{"bibtype":"phdthesis","type":"phdthesis","author":[{"firstnames":["Vanessa","Wei"],"propositions":[],"lastnames":["Feng"],"suffixes":[]}],"title":"RST-Style Discourse Parsing and Its Applications in Discourse Analysis","school":"Department of Computer Science, University of Toronto","year":"2015","abstract":"Discourse parsing is the task of identifying the relatedness and the particular discourse relations among various discourse units in a text. In particular, among various theoretical frameworks of discourse parsing, I am interested in Rhetorical Structure Theory (RST). I hypothesize that, given its ultimate success, discourse parsing can provide a general solution for use in many downstream applications. <P> This thesis is composed of two major parts. First, I overview my work on discourse segmentation and discourse tree-building, which are the two primary components of RST-style discourse parsing. Evaluated on the RST Discourse Treebank (RST-DT), both of my discourse segmenter and tree-builder achieve the state-of-the-art performance. <P> Later, I discuss the application of discourse relations to some specific tasks in the analysis of discourse, including the evaluation of coherence, the identification of authorship, and the detection of deception. In particular, I propose to use a set of application-neutral features, which are derived from the discourse relations extracted by my discourse parser, and compare the performance of these application-neutral features against the classic application-specific approaches to each of these tasks. On the first two tasks, experimental results show that discourse relation features by themselves often perform as well as those classic application-specific features, and the combination of these two kinds of features usually yields further improvement. These results provide strong evidence for my hypothesis that discourse parsing is able to provide a general solution for the analysis of discourse. However, we failed to observe a similar effectiveness of discourse parsing on the third task, the detection of deception. I postulate that this might be due to several confounding factors of the task itself.","download":"http://ftp.cs.toronto.edu/pub/gh/Feng-thesis-2015.pdf","bibtex":"@phdthesis{FengPhD,\n author = \"Vanessa Wei Feng\",\n title = \"RST-Style Discourse Parsing and Its Applications in Discourse Analysis\",\n school = \"Department of Computer Science, University of Toronto\",\n year = \"2015\",\n abstract = \"Discourse parsing is the task of identifying the\n relatedness and the particular discourse relations\n among various discourse units in a text. In\n particular, among various theoretical frameworks of\n discourse parsing, I am interested in Rhetorical\n Structure Theory (RST). I hypothesize that, given\n its ultimate success, discourse parsing can provide\n a general solution for use in many downstream\n applications. <P> This thesis is composed of two\n major parts. First, I overview my work on discourse\n segmentation and discourse tree-building, which are\n the two primary components of RST-style discourse\n parsing. Evaluated on the RST Discourse Treebank\n (RST-DT), both of my discourse segmenter and\n tree-builder achieve the state-of-the-art\n performance. <P> Later, I discuss the application\n of discourse relations to some specific tasks in the\n analysis of discourse, including the evaluation of\n coherence, the identification of authorship, and the\n detection of deception. In particular, I propose to\n use a set of application-neutral features, which are\n derived from the discourse relations extracted by my\n discourse parser, and compare the performance of\n these application-neutral features against the\n classic application-specific approaches to each of\n these tasks. On the first two tasks, experimental\n results show that discourse relation features by\n themselves often perform as well as those classic\n application-specific features, and the combination\n of these two kinds of features usually yields\n further improvement. These results provide strong\n evidence for my hypothesis that discourse parsing is\n able to provide a general solution for the analysis\n of discourse. However, we failed to observe a\n similar effectiveness of discourse parsing on the\n third task, the detection of deception. I postulate\n that this might be due to several confounding\n factors of the task itself.\", \n download = \"http://ftp.cs.toronto.edu/pub/gh/Feng-thesis-2015.pdf\"\n}\n\n\n","author_short":["Feng, V. W."],"key":"FengPhD","id":"FengPhD","bibbaseid":"feng-rststylediscourseparsinganditsapplicationsindiscourseanalysis-2015","role":"author","urls":{},"downloads":0},"search_terms":["rst","style","discourse","parsing","applications","discourse","analysis","feng"],"keywords":[],"authorIDs":[],"dataSources":["2vBSdbWEoTEQZtb6g"]}