Rajen Subba

An effective Discourse Parser that uses Rich Linguistic Information

This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying relations over attribute-value learning paradigms such as Decision Trees, RIPPER and Naive Bayes. For discourse parsing, our modified shift-reduce parsing model that uses our relation classifier significantly outperforms a right-branching majority-class baseline.

Detecting Life Events in Feeds from Twitter

Short posts on micro-blogs are characterized by high ambiguity and non-standard language. We focus on detecting life events from such micro-blogs, a type of event which have not been paid much attention so far. We discuss the corpus we assembled and our experiments. Simpler models based on unigrams perform better than models that include history, number of retweets and semantic roles.

The problem of ontology alignment on the web: a first report

This paper presents a general architecture and four algorithms that use Natural Language Processing for automatic ontology matching. The proposed approach is purely instance based, i.e., only the instance documents associated with the nodes of ontologies are taken into account. The four algorithms have been evaluated using real world test data, taken from the Google and LookSmart online directories. The results show that NLP techniques applied to instance documents help the system achieve higher performance.