Dan Jurafsky

TL;DR: A new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases that shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained).

TL;DR: A new approach that combines accent detection, accent discriminative acoustic features, acoustic adaptation and model selection for accented Chinese speech recognition is proposed and experimental results show that this approach can improve the recognition of accented speech.

TL;DR: A discriminative classifier is trained over a wide variety of features derived from WordNet structure, corpus-based evidence, and evidence from other lexical resources, and a learned similarity measure outperforms previously proposed automatic methods for sense clustering on the task of predicting human sense merging judgments.

TL;DR: It is demonstrated that portraying the self, whether as well–educated, as a victim, or even as addicted to chocolate, is a key function of reviews and suggests the important role of online reviews in exploring social psychological variables.

TL;DR: This article provided an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force, and found evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice.