Nowadays, many health groups and forums are established on the Internet, where health consumers discuss health issues and interact with each other. Although there is a large amount of user generated content about healthcare on different social media sites, few studies have applied data mining or artificial intelligence techniques for knowledge discovery on a large scale of data in this particul...

The goal of our project is to develop an accurate tagger for questions posted on Stack Exchange. Our problem is an instance of the more general problem of developing accurate classifiers for large scale text datasets. We are tackling the multilabel classification problem where each item (in this case, question) can belong to multiple classes (in this case, tags). We are predicting the tags (or ...

This paper focuses on the relationship between source text characteristics (ambiguity, complexity and style compliance) and machine-translation post-editing effort (both temporal and technical). Post-editing data is collected in a traditional translation environment and subsequently plotted against textual scores produced by a range of systems. Our findings show some strong correlation between ...

This article explores the use of text simplification as a pre-processing step for statistical machine translation of grammatically complex under-resourced languages. Our experiments on English-to-Serbian translation show that this approach can improve grammaticality (fluency) of the translation output and reduce technical post-editing effort (number of post-edit operations). Furthermore, the us...

These are class notes for a PhD level course on Bayesian nonparametrics, taught at Columbia University in Fall 2013. This text is a draft. I apologize for the many typos, false statements and poor explanations no doubt still left in the text; I will post corrections as they become available.

We take part in the short text conversation task at NTCIR-12. We employ a semantic-based retrieval method to tackle this problem, by calculating textual similarity between posts and comments. Our method applies a rich-feature model to match post-comment pairs, by using semantic, grammar, n-gram and string features to extract high-level semantic meanings of text.