Home > Unit > Computer Science

Computer Science

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.

Rapid fire roundup of ongoing research in the CLIP Lab.

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.

Title- Interpretable Machine Learning: What it means, How we're getting there


Title: Using deep learning to understand creative language


Note new time--12:30pm!

Defining and Parsing Universal Dependencies 

Pages

Subscribe to Computer Science