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.
296970
Integrating Web-based and Corpus-based Techniques for Question Answering
MIT CSAIL’s entry in this year’s TREC Question Answering track focused on integrating Web-based techniques with more traditional strategies based on document retrieval and named-entity detection. We believe that achieving high performance in the question answering task requires a combination of multiple strategies designed to capitalize on different characteristics of various resources. The system we deployed for the TREC evaluation last year relied exclusively on the World Wide Web to answer factoid questions (Lin et al., 2002). The advantages that the Web offers are well known and have been exploited by previous systems (Brill et al., 2001; Clarke et al., 2001; Dumais et al., 2002). The immense amount of freely available unstructured text provides data redundancy, which can be leveraged with simple pattern matching techniques involving the expected answer formulations. In many ways, we can utilize huge quantities of data to overcome many thorny problems in natural language processing such as lexical ambiguity and paraphrases. Furthermore, Web search engines such as Google provide a convenient front-end for accessing and filtering enormous amounts of Web data. We have identified this class of techniques as the knowledge mining approach to question answering (Lin and Katz, 2003). In addition to viewing the Web as a repository of unstructured documents, we can also take advantage of structured and semistructured sources available on the Web using knowledge annotation techniques (Katz, 1997; Lin and Katz, 2003). Through empirical analysis of real world natural language questions, we have noticed that large classes of commonly occurring queries can be parameterized and captured using a simple object–property–value data model (Katz et al., 2002). Furthermore, such a data model is easy to impose on Web resources through a framework of wrapper scripts. These techniques allow our system to view the Web as if it were a “virtual database” and use knowledge contained therein to answer user questions. While the Web is undeniably a useful resource for question answering, it is not without drawbacks. Useful knowledge on the Web is often drowned out by the sheer amount of irrelevant material, and statistical techniques are often insufficient to separate right answers from wrong ones. Overcoming these obstacles will require addressing many outstanding issues in computational linguistics: anaphora resolution, paraphrase normalization, temporal reference calculation, and lexical disambiguation, just to name a few. Furthermore, the setup of the TREC evaluations necessitates an extra step in the question answering process for systems that extract answers from external sources, typically known as answer projection. For every Web-derived answer, a system must find a supporting document from the AQUAINT corpus, even if the corpus was not used in the answer extraction process. This year’s main task included definition and list questions in addition to factoid questions. Although Web-based techniques have proven effective in handling factoid questions, they are less applicable to tackling definition and list questions. The datadriven approach implicitly assumes that each natural language question has a unique answer. Since a single answer instance is sufficient, algorithms were designed to trade recall for precision. For list and definition questions, however, a more balanced approach is required, since multiple answers are not only desired, but necessary. We believe that the best strategy is to integrate Web-based approaches with more traditional question answering techniques driven by document retrieval and named-entity detection. Corpusand Web-based strategies should play complementary roles in an overall question answering framework.
6064636
Automatically Evaluating Answers to Definition Questions
Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called Pourpre, for automatically evaluating answers to definition questions. 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 and TREC 2004 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that Pourpre outperforms direct application of existing metrics.
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.
7230205
Answering Questions about Moving Objects in Surveillance Videos
Current question answering systems succeed in many respects regarding questions about textual documents. However, information exists in other media, which provides both opportunities and challenges for question answering. We present results in extending question answering capabilities to video footage captured in a surveillance setting. Our prototype system, called Spot, can answer questions about moving objects that appear within the video. We situate this novel application of vision and language technology within a larger framework designed to integrate language and vision systems under a common representation. We believe that our framework will support the next generation of multimodal natural language information access systems.
12964519
Development and evaluation of emerging design patterns for ubiquitous computing
Design patterns are a format for capturing and sharing design knowledge. In this paper, we look at a new domain for design patterns, namely ubiquitous computing. The overall goal of this work is to aid practice by speeding up the diffusion of new interaction techniques and evaluation results from researchers, presenting the information in a form more usable to practicing designers. Towards this end, we have developed an initial and emerging pattern language for ubiquitous computing, consisting of 45 pre-patterns describing application genres, physical-virtual spaces, interaction and systems techniques for managing privacy, and techniques for fluid interactions. We evaluated the effectiveness of our pre-patterns with 16 pairs of designers in helping them design location-enhanced applications. We observed that our pre-patterns helped new and experienced designers unfamiliar with ubiquitous computing in generating and communicating ideas, and in avoiding design problems early in the design process.
2759655
Cha-Cha: A System for Organizing Intranet Search Results
Although search over World Wide Web pages has recently received much academic and commercial attention, surprisingly little research has been done on how to search the web pages within large, diverse intranets. Intranets contain the information associated with the internal workings of an organization. 
 
A standard search engine retrieves web pages that fall within a widely diverse range of information contexts, but presents these results uniformly, in a ranked list. As an alternative, the Cha-Cha system organizes web search results in such a way as to reflect the underlying structure of the intranet. In our approach, an "outline" or "table of contents" is created by first recording the shortest paths in hyperlinks from root pages to every page within the web intranet. After the user issues a query, these shortest paths are dynamically combined to form a hierarchical outline of the context in which the search results occur. The system is designed to be helpful for users with a wide range of computer skills. Preliminary user study and survey results suggest that some users find the resulting structure more helpful than the standard retrieval results display for intranet search.
5232648
Koala: capture, share, automate, personalize business processes on the web
We present Koala, a system that enables users to capture, share, automate, and personalize business processes on the web. Koala is a collaborative programming-by-demonstration system that records, edits, and plays back user interactions as pseudo-natural language scripts that are both human- and machine-interpretable. Unlike previous programming by demonstration systems, Koala leverages sloppy programming that interprets pseudo-natural language instructions (as opposed to formal syntactic statements) in the context of a given web page's elements and actions. Koala scripts are automatically stored in the Koalescence wiki, where a community of users can share, run, and collaboratively develop their "how-to" knowledge. Koala also takes advantage of corporate and personal data stores to automatically generalize and instantiate user-specific data, so that scripts created by one user are automatically personalized for others. Our initial experiences suggest that Koala is surprisingly effective at interpreting instructions originally written for people.
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.
16138730
Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data
In this work, we take the traditional notation of contrast sets and extend them to other data types, in particular time series and by extension, images. In the traditional sense, contrast-set mining identifies attributes, values and instances that differ significantly across groups, and helps user understand the differences between groups of data. We reformulate the notion of contrast-sets for time series data, and define it to be the key pattern(s) that are maximally different from the other set of data. We propose a fast and exact algorithm to find the contrast sets, and demonstrate its utility in several diverse domains, ranging from industrial to anthropology. We show that our algorithm achieves 3 orders of magnitude speedup from the brute-force algorithm, while producing exact solutions.