Argumentation is an interactive process and frequently occurs in conversations. Social networks and online debates provide a two-way communication platform with a huge amount of opinion and argument rich information. However, the massive amount of information available today is overwhelming to our brain and its ability to efficiently absorb and comprehend. Identifying and integrating arguments across these discussions requires computational methods that can facilitate users to systematically search, analyze and summarize arguments as well as reason about the relationship among arguments. We develop techniques to recognize the specific arguments and counter arguments people tend to advance and group them across discussions as Facets. This entails two sub-tasks: (i) Extract and summarize important arguments, and (ii) Discover similar repeated argument aspects that appear across multiple dialogues on a topic. We present a systematic approach to leverage pyramid based summarization framework to identify central propositions as those arguments that people find most salient, and then rank and select arguments in social media dialogue, which is a novel method for ranking arguments in conversational data. Our results show that adding contextual knowledge from a dialogue improves argument extraction. We introduce a new task of Argument Facet Similarity (AFS), where we develop a new corpus aimed at identifying various facets across opinionated dialogue. A graded argument similarity model was defined that takes as input two sentential arguments and returns a scalar value that categorizes their similarity (AFS). The prediction output obtained from the proposed model improves the results obtained from previous work that defines similarity of short texts as Semantic Textual Similarity (STS).

The natural instability and breakup of electrified cone jets leads to the formation of chargeddroplets with size and charge distribution of the droplets governed by the physical properties and electrospraying conditions under consideration. This thesis develops a leaky-dielectric model to study the axisymmetric breakup of an electrified jet, using the phase field method to treat interfacial phenomena. The model is used to analyze the breakup in a wide range of the Taylor number (Γ), the Ohnesorge number (Oh) and the wavenumber relevant to electrosprays operating in the cone-jet mode. The phase field technique accurately captures the behavior of the jet after pinch-off and predicts the formation of primary and satellite droplets. The numerical results are compared with existing experimental and numerical studies, extending them to account for the formation of sub-satellite droplets. It is found that for highly viscous jets, Oh ≫ 1, the number of sub-satellite droplets generated increases with the Taylor number when compared to low viscous jets, hence widening the size distribution of droplets. At fixed Γ and Oh the primary droplets are charged to an approximately constant ratio of the Rayleigh charge limit, regardless of the wave number. Furthermore, the primary droplets are charged below the Rayleigh limit for Γ ≲ 1.5, and charged above the Rayleigh limit when Γ ≳ 1.5. Thus, most primary droplets are expected to be unstable at Taylor numbers exceeding 1.5.