Showing papers by "Kevin Duh published in 2009"

Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing

Abstract: Welcome to the NAACL HLT Workshop on Semi-supervised Learning for Natural Language Processing! Will semi-supervised learning (SSL) become the next de-facto standard for building natural language processing (NLP) systems, just as supervised learning has transformed the field in the last decade? Or will it remain as a nice idea that doesn't always work in practice? Semi-supervised learning has become an important topic due to the promise that high-quality labeled data and abundant unlabeled data, if leveraged appropriately, can achieve superior performance at lower cost. As researchers in semi-supervised learning reach critical mass, we believe it is time to take a step back and think broadly about whether we can discover general insights from the various techniques developed for different NLP tasks. The goal of this workshop is to help build a community of SSL-NLP researchers and foster discussions about insights, speculations, and results (both positive and negative) that may otherwise not appear in a technical paper at a major conference. In our call-for-paper, we posed some open questions: 1. Problem Structure: What are the different classes of NLP problem structures (e.g. sequences, trees, N-best lists) and what algorithms are best suited for each class? For instance, can graph-based algorithms be successfully applied to sequence-to-sequence problems like machine translation, or are self-training and feature-based methods the only reasonable choices for these problems? 2. Background Knowledge: What kinds of NLP-specific background knowledge can we exploit to aid semi-supervised learning? Recent learning paradigms such as constraint-driven learning and prototype learning take advantage of our domain knowledge about particular NLP tasks; they represent a move away from purely data-agnostic methods and are good examples of how linguistic intuition can drive algorithm development. 3. Scalability: NLP data-sets are often large. What are the scalability challenges and solutions for applying existing semi-supervised learning algorithms to NLP data? 4. Evaluation and Negative Results: What can we learn from negative results? Can we make an educated guess as to when semi-supervised learning might outperform supervised or unsupervised learning based on what we know about the NLP problem? 5. To Use or Not To Use: Should semi-supervised learning only be employed in low-resource languages/tasks (i.e. little labeled data, much unlabeled data), or should we expect gains even in high-resource scenarios (i.e. expecting semi-supervised learning to improve on a supervised system that is already more than 95% accurate)? We received 17 submissions and selected 10 papers after a rigorous review process. These papers cover a variety of tasks, ranging from information extraction to speech recognition. Some introduce new techniques, while others compared existing methods under a variety of situations. We are pleased to present these papers in this volume.