7550683
Will Pyramids Built of Nuggets Topple Over?
The present methodology for evaluating complex questions at TREC analyzes answers in terms of facts called "nuggets". The official F-score metric represents the harmonic mean between recall and precision at the nugget level. There is an implicit assumption that some facts are more important than others, which is implemented in a binary split between "vital" and "okay" nuggets. This distinction holds important implications for the TREC scoring model---essentially, systems only receive credit for retrieving vital nuggets---and is a source of evaluation instability. The upshot is that for many questions in the TREC testsets, the median score across all submitted runs is zero. In this work, we introduce a scoring model based on judgments from multiple assessors that captures a more refined notion of nugget importance. We demonstrate on TREC 2003, 2004, and 2005 data that our "nugget pyramids" address many shortcomings of the present methodology, while introducing only minimal additional overhead on the evaluation flow.
22235434
The role of context in question answering systems
Despite recent advances in natural language question an-swering technology, the problem of designing effective user interfaces has been largely unexplored. We conducted a user study to investigate the problem and discovered that overall, users prefer a paragraph-sized chunk of text over just an exact phrase as the answer to their questions. Fur-thermore, users generally prefer answers embedded in con-text, regardless of the perceived reliability of the source documents. When users research a topic, increasing the amount of text returned to users significantly decreases the number of queries that they pose to the system, suggesting that users utilize supporting text to answer related ques-tions. We believe that these results can serve to guide future developments in question answering user interfaces.
9391992
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.
12592432
User simulations for evaluating answers to question series
Recently, question series have become one focus of research in question answering. These series are comprised of individual factoid, list, and ''other'' questions organized around a central topic, and represent abstractions of user-system dialogs. Existing evaluation methodologies have yet to catch up with this richer task model, as they fail to take into account contextual dependencies and different user behaviors. This paper presents a novel simulation-based methodology for evaluating answers to question series that addresses some of these shortcomings. Using this methodology, we examine two different behavior models: a ''QA-styled'' user and an ''IR-styled'' user. Results suggest that an off-the-shelf document retrieval system is competitive with state-of-the-art QA systems in this task. Advantages and limitations of evaluations based on user simulations are also discussed.
16198992
Evaluation of resources for question answering evaluation
Controlled and reproducible laboratory experiments, enabled by reusable test collections, represent a well-established methodology in modern information retrieval research. In order to confidently draw conclusions about the performance of different retrieval methods using test collections, their reliability and trustworthiness must first be established. Although such studies have been performed for ad hoc test collections, currently available resources for evaluating question answering systems have not been similarly analyzed. This study evaluates the quality of answer patterns and lists of relevant documents currently employed in automatic question answering evaluation, and concludes that they are not suitable for post-hoc experimentation. These resources, created from runs submitted by TREC QA track participants, do not produce fair and reliable assessments of systems that did not participate in the original evaluations. Potential solutions for addressing this evaluation gap and their shortcomings are discussed.
6918069
Methods for automatically evaluating answers to complex questions
Evaluation is a major driving force in advancing the state of the art in language technologies. In particular, methods for automatically assessing the quality of machine output is the preferred method for measuring progress, provided that these metrics have been validated against human judgments. Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called POURPRE, an automatic technique for evaluating answers to complex questions based on n-gram co-occurrences between machine output and a human-generated answer key. Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information “nugget” appears in a system's response. The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003, TREC 2004, and TREC 2005 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that POURPRE outperforms direct application of existing metrics.
6644016
Multistar Decomposition of Complete Multigraphs
2209704
Capturing natural hand articulation
Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
1747696
HOT SAX: efficiently finding the most unusual time series subsequence
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Time series discords have many uses for data mining, including improving the quality of clustering, data cleaning, summarization, and anomaly detection. Discords are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. We evaluate our work with a comprehensive set of experiments. In particular, we demonstrate the utility of discords with objective experiments on domains as diverse as Space Shuttle telemetry monitoring, medicine, surveillance, and industry, and we demonstrate the effectiveness of our discord discovery algorithm with more than one million experiments, on 82 different datasets from diverse domains.
1721157
Modeling the constraints of human hand motion
Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-efficiently when considering the motion constraints of a hand. Although some constraints can be represented as equalities or inequalities, there exist many constraints which cannot be explicitly represented. In this paper, we propose a learning approach to model the hand configuration space directly. The redundancy of the configuration space can be eliminated by finding a lower-dimensional subspace of the original space. Finger motion is modeled in this subspace based on the linear behavior observed in the real motion data collected by a CyberGlove. Employing the constrained motion model, we are able to efficiently capture finger motion from video inputs. Several experiments show that our proposed model is helpful for capturing articulated motion.
8597170
Approximations to magic: finding unusual medical time series
In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is 3 to 4 orders of magnitude faster than brute force, while guaranteed to produce identical results.