Justin Betteridge

Coupled semi-supervised learning for information extraction

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.

Community-based classification of noun phrases in twitter

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the authors behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.

Capturing knowledge from domain text with controlled language

This paper describes a prototype system which captures semantic knowledge from domain text using controlled language. The KANTOO system is used to analyze input sentences from college-level science textbooks, producing sentence-level meaning representations (interlingua). The interlingua expressions are mapped into F-logic statements, which are be stored in a separate knowledge base to support reasoning in the domain.