Michele Banko

Web question answering: is more always better?

This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines.

An Analysis of the AskMSR Question-Answering System

We describe the architecture of the AskMSR question answering system and systematically evaluate contributions of different system components to accuracy. The system differs from most question answering systems in its dependency on data redundancy rather than sophisticated linguistic analyses of either questions or candidate answers. Because a wrong answer is often worse than no answer, we also explore strategies for predicting when the question answering system is likely to give an incorrect answer.

Part of speech tagging in context

We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons. Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. Finally, we show how this new tagger achieves state-of-the-art results in a supervised, non-training intensive framework.

Mitigating the paucity-of-data problem: exploring the effect of training corpus size on classifier performance for natural language processing

In this paper, we discuss experiments applying machine learning techniques to the task of confusion set disambiguation, using three orders of magnitude more training data than has previously been used for any disambiguation-in-string-context problem. In an attempt to determine when current learning methods will cease to benefit from additional training data, we analyze residual errors made by learners when issues of sparse data have been significantly mitigated. Finally, in the context of our results, we discuss possible directions for the empirical natural language research community.

Using N-Grams to understand the nature of summaries

Although single-document summarization is a well-studied task, the nature of multi-document summarization is only beginning to be studied in detail. While close attention has been paid to what technologies are necessary when moving from single to multi-document summarization, the properties of human-written multi-document summaries have not been quantified. In this paper, we empirically characterize human-written summaries provided in a widely used summarization corpus by attempting to answer the questions: Can multi-document summaries that are written by humans be characterized as extractive or generative? Are multi-document summaries less extractive than single-document summaries? Our results suggest that extraction-based techniques which have been successful for single-document summarization may not be sufficient when summarizing multiple documents.