This paper explores the close relationship between question answering and machine reading, and how the active use of reasoning to answer (and in the process, disambiguate) questions can also be applied to reading declarative texts, where a substantial proportion of the text's contents is already known to (represented in) the system. In question answering, a question may be ambiguous, and it may only be in the process of trying to answer it that the "right" way to disambiguate it becomes apparent. Similarly in machine reading, a text may be ambiguous, and may require some process to relate it to what is already known. Our conjecture in this paper is that these two processes are similar, and that we can modify a question answering tool to help "read" new text that augments existing system knowledge. Specifically, interpreting a new text T can be recast as trying to answer, or partially answer, the question "Is it true that T?", resulting in both appropriate disambiguation and connection of T to existing knowledge. Some preliminary investigation suggests this might be useful for proposing knowledge base extensions, extracted from text, to a knowledge engineer.

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Machine Reading as a Process of Partial Question-Answering

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.

Qin Iris Wang

Papers

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