Computational Aspects of Discourse Analysis

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MOTIVATION*"Writing an NSERC DG application takes time... Jane wrote hers in two months." *In a coherent text, textual units are not understood in isolation but in relation with each other through discourse relations that may or may not be explicitly marked. The fact that Jane wrote her application in two months, "illustrates" that writing a NSERC DG application is long! Research on discourse analysis tries to model such relations, which allows us to interpret the text and understand the communicative purpose of its units. This, in turn, is useful for many Natural Language Processing applications such as automatic summarization, text simplification...**LONG TERM OBJECTIVE*Discourse coherence refers to the logical connections between textual units (for example, "illustration", "result", "purpose"...). Although, much work has been done in recent years, computational discourse analysis is still in its infancy and many important open questions still remain. The long term objective of this research program is to explore computational aspects of automatic discourse analysis. **SCIENTIFIC APPROACH*This research will explore 3 specific questions: **1) The exploration of the effect of genre on discourse tagging: Here, we will explore how the textual genre (e.g. political, procedural, scientific,...) affects the use of discourse relations. There are significant differences in the linguistic realization of relations across domains and genres that need to be captured and modeled. The types of questions we will address include: What is the influence of the textual genre on the usage of relations and choice of discourse markers? What is the interaction between global discourse structures and local relations? Can we identify stereotypical patterns of local relations in particular textual genres? This work will identify and measure the correlation between linguistic features (lexical, contextual, syntactic and semantic information) and textual genres which will be used to tailor discourse taggers to a specific genre.**2) Unsupervised discourse tagging across languages: Here we will investigate how the usage and linguistic realization of relations vary across languages. We will perform a cross-lingual comparison of discourse relations in English and French, and focus on two questions: Is the usage of discourse relations language-independent? How do discourse relations align across languages? This work will result in a large cross-lingual resource which we will make available to the research community and will be used to induce discourse parsers in one language from other parsers in another language.**3) The use of discourse analysis for text simplification: Here we will explore how discourse analysis can be used to improve simplify texts automatically to make them accessible to a wider audience regardless of their language skills. In particular, we will address two questions: Can we achieve text simplification by signaling implicit relations more explicitly? Can we achieve text simplification by pruning less informative text spans?**SIGNIFICANCE OF THE WORK*With the current state of the art in Natural Language Processing research, work on computational aspects of discourse analysis has now become possible and is growing much interest in the research community. Addressing this issue will allow us to go beyond understanding the literal meaning of a text, but interpret its "deep" meaning or communicative intention. Concretely, our work can be beneficial to all sectors of the Canadian language industry: in speech processing, machine-aided translation, content management, language e-learning... With all these potential applications, I am confident that our work can easily be transferred to the Canadian language industry.