Computer Science
Title: Policy and Regulatory Semantics for Compliance Assistance
Title: Duolingo: Improving Language Learning and Assessment with Data
Title: Separate the Sheep from the Goats: On recognizing the Literal and Figurative Usages of Idioms
Title: Classification and clustering based on LDA-like models
Abstract: I introduce two studies used statistical model with latent variables similar to Latent Dirichllet Allocation (LDA) model.
Title: Semantic and Stylistic Variations in Machine Translation
Abstract: While parallel texts represent invaluable resources for machine translation, they inevitably introduce biases in the cross-lingual mappings learned by machine translation models. In addition to the domain bias and translationese bias studied in past work, we argue that another form of bias arises from subtle choices in content and style made by translators to appropriately convey the meaning of the source to their target audience.
Title: Weakly Supervised Learning for Detection of Online Harassment
Title: Text Analytics in Finance: A Case Study and Some Considerations
Abstract: The finance industry increasingly seeks insight from unstructured data, including through text analytics. In this talk, I will give a brief survey of NLP as used in text analytics, then talk in detail about the NLP platform we are building at Bloomberg, including example applications. I will close with some ways in which NLP for financial text analytics is similar to and different from NLP as commonly done in research, and some ideas for productive NLP work.
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