Meaning depends on context. This applies both in obviouscases like deictics or sarcasm as well as more subtle situationslike framing or persuasion. One key characteristic of context isthe identity of the participants in an interaction. Our interpre-tation of an utterance depends on a variety of factors such asour personal history, background knowledge, and our relation-ship to the source. While demographics allow us to capturesome of this variance, the relevance of specific demographicfactors varies across contexts. To address these challenges, weintroduce a method for combining demographics and contextinto situated demographic embeddings—mapping representa-tions onto a continuous space appropriate for the given domain.We further demonstrate how to make use of related externalresources so as to apply this approach in low-resource situa-tions. We show the resulting representations to be interpretableand consider domain-specific similarity. Finally, we show howthese representations can be incorporated to improve modelingof a real-world natural language understanding task.

It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstrate that participants with access to background knowledge (experts), overall performed significantly better than those without access to this knowledge (novices). More importantly, we show that experts rely more on relational features than surface features, an aspect that bagof- words methods fail to capture. We then propose a computational model for text classification which incorporates background knowledge. This model is built upon vector-based representation methods and achieves significantly more accurate results over other models that were tested.

The psycholinguistic theory of Communication Accommoda-tion proposes that people modify communication dynamics(e.g. vocal patterns, gesture, word choice, syntax, etc.) tominimize (or maximize) their social differences. Research oncommunication accommodation has shown that people whowant social approval will modify their linguistic style to matchthat of their interactant; however, most studies have been con-ducted on small-scale datasets and in laboratory situations. Inthis work, we investigate the relationship between linguisticsyntactic usage and conversation participation in a more nat-uralistic conversational setting: social media conversations onReddit.com. We introduce a novel approach for calculatingdocument-level syntactic similarity by relying on natural lan-guage processing methods (parse tree generators) and graphtheory techniques (minimum weight perfect matching on com-plete bipartite graphs). Using the proposed method, we presentthe results of two experiments which demonstrate that userswho comment on a post tend to use syntax similar to that ofthe original post. Specifically, we provide evidence that com-ments on a post are more likely to follow the syntactic structureof the original post, compared to both random comments andalso posts by the author of the comment.