Abstract Query formation and expansion is an integral part of nearly every effort to search for information. In the work reported here we investigate the effects of domain knowledge and feedback on search term selection and reformation. We explore differences between experts and novices as they generate search terms over 10 successive trials and under two feedback conditions. Search attempts were coded on quantitative dimensions such as the number of unique terms and average time per trial, and as a whole in an attempt to characterize the user's conceptual map for the topic under differing conditions of participant‐defined domain expertise. Nine distinct strategies were identified. Differences emerged as a function of both expertise and feedback. In addition, strategic behavior varied depending on prior search conditions. The results are considered from both a theoretical and design perspective, and have direct implications for digital library usability and metadata generation, and query expansion systems.

In this poster, we will present the results of efforts we have undertaken to conduct evaluations of a QA system in a real world environment and to understand the nature of the dimensions on which users evaluate QA systems when given full reign to comment on whatever dimensions they deem important.

The poster reports on a project in which we are investigating methods for breaking the human metadata-generation bottleneck that plagues Digital Libraries. The research question is whether metadata elements and values can be automatically generated from the content of educational resources, and correctly assigned to mathematics and science educational materials. Natural Language Processing and Machine Learning techniques were implemented to automatically assign values of the GEMgenerate metadata element set tofor learning resources provided by the Gateway for Education (GEM), a service that offers web access to a wide range of educational materials. In a user study, education professionals evaluated the metadata assigned to learning resources by either automatic tagging or manual assignment. Results show minimal difference in the eyes of the evaluators between automatically generated metadata and manually assigned metadata.

One way to think about what decision-making will be like in the future is to look at it in terms of the potential combinatorial power of adaptive, intelligent agents with the significant accomplishments of the burgeoning Natural Language Processing (NLP) capabilities of today in technologies that will significantly impact human decision-making, decision support systems, and potentially fully automated decision making.

Text categorization is the problem of automatically assigning predefined categories to natural language texts. A major difficulty of this problem stems from the high dimensionality of its feature space. Reducing the dimensionality, or selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In this paper, we propose a neuro-genetic approach to feature selection in text categorization. Candidate feature subsets are evaluated by using three-layer feedforward neural networks. The Baldwin effect concerns the tradeoffs between learning and evolution. It is used in our research to guide and improve the GA-based evolution of the feature subsets. Experimental results show that our neuro-genetic algorithm is able to perform as well as, if not better than, the best results of neural networks to date, while using fewer input features.

Article Free Access Share on Posters: abstracts Author: Elizabeth D. Liddy School of Information Studies, Syracuse University, 4-206 Center for Science & Technology, Syracuse, NY School of Information Studies, Syracuse University, 4-206 Center for Science & Technology, Syracuse, NYView Profile Authors Info & Claims SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrievalAugust 1996 https://doi.org/10.1145/243199.250974Published:18 August 1996Publication History 0citation193DownloadsMetricsTotal Citations0Total Downloads193Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The text categorization module described here provides a front-end filtering function for the larger DR-LINK text retrieval system [Liddy and Myaeing 1993]. The model evaluates a large incoming stream of documents to determine which documents are sufficiently similar to a profile at the broad subject level to warrant more refined representation and matching. To accomplish this task, each substantive word in a text is first categorized using a feature set based on the semantic Subject Field Codes (SFCs) assigned to individual word senses in a machine-readable dictionary. When tested on 50 user profiles and 550 megabytes of documents, results indicate that the feature set that is the basis of the text categorization module and the algorithm that establishes the boundary of categories of potentially relevant documents accomplish their tasks with a high level of performance. This means that the category of potentially relevant documents for most profiles would contain at least 80% of all documents later determined to be relevant to the profile. The number of documents in this set would be uniquely determined by the system's category-boundary predictor, and this set is likely to contain less than 5% of the incoming stream of documents.

This study investigated two hypotheses concerning the use of anaphors in information retrieval. The first hypothesis, that anaphors tend to refer to integral concepts rather than to peripheral concepts, was well supported. Two samples of documents, one in psychology and the other in computer science, were examined by subject experts who judged the centrality of phrases which were referred to anaphorically. The second hypothesis, that various term weighting schemes are affected differently by anaphoric resolution, was also well supported. It was found that schemes which incorporate document length into the calculations produce much smaller increases in term weights for terms occurring in anaphoric resolutions than do those which do not consider document length. It is concluded that although anaphoric resolution has potential for better representing the “aboutness” of a document, care must be taken in choosing both the anaphoric classes to be resolved and the term weighting schemes to be used in measuring a document's topicality.

Natural language texts are used extensively in a range of information science tasks. Such use requires increased attention to discourse level linguistic phenomena which have the potential for impact on these tasks. One such device, anaphoric reference, was investigated in a frequently used text type, namely, scientific abstracts. Descriptive data on the extent of use of discourse anaphora in abstracts was gathered and rules for distinguishing anaphoric functioning of terms were compiled and tested. Results show a mean use of 3.67 functioning anaphors per abstract in a random sample of 600 abstracts from two databases. Testing of rules indicates high feasibility of future algorithmic recognition of anaphoric uses of terms. © 1987 John Wiley & Sons, Inc.