Comparing noun phrasing techniques for use with medical digital library tools

Tolle, Kristin M.; Chen, Hsinchun

doi:10.1002/(sici)1097-4571(2000)51:4<352::aid-asi5>3.0.co;2-8

Cited by 77 publications

(43 citation statements)

References 20 publications

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“…Increasing computing power and the recent progress in text mining research in general, and automatic text interpretation in the form of text mining in particular [19], have given further opportunities to reveal different levels of sentiment from written text. This resulted in the development of two different approaches for textual usage.…”

Section: Human Sentiment In Behaviroal Financementioning

confidence: 99%

Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

Crone

Koeppel

2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioural finance.Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioural finance. This study explores the efficacy of using novel sentiment indicators from MarketPsych, which analyses social media in addition to newsfeeds to quantify various levels of individual's emotions, as a predictor for financial time series returns of the Australian Dollar (AUD) -US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioural finance, combining technical and behavioural aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares multivariate linear regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation.

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Section: Human Sentiment In Behaviroal Financementioning

confidence: 99%

Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

Crone

Koeppel

2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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“…Measuring precision and recall against author keyphrases is easy to carry out, and it allows comparisons between different keyphrase extraction systems. Previous studies have used this measure and found it to be an appropriate method to measure the effectiveness of a keyphrase extraction system (Jones and Paynter 2002;Turney 2000;Jones and Mahoui 2000;Tolle and Chen 2000).…”

Section: Kip's Performancementioning

confidence: 99%

Document keyphrases as subject metadata: incorporating document key concepts in search results

2008

Inf Retrieval

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Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially.

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“…It having been shown that automatic indexing is as effective as human indexing [18], many proven techniques have been developed. Linguistics approaches such as noun phrasing also have been applied to perform indexing for phrases rather than just words [21]. These techniques are useful in extracting meaningful terms from text documents not only for document retrieval but also for further analysis.…”

Section: Indexing and Categorizationmentioning

confidence: 99%

Personalized spiders for web search and analysis

Chau

Zeng

Chen

2001

Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries

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Searching for useful information on the World Wide Web has become increasingly difficult. While Internet search engines have been helping people to search on the web, low recall rate and outdated indexes have become more and more problematic as the web grows. In addition, search tools usually present to the user only a list of search results, failing to provide further personalized analysis which could help users identify useful information and comprehend these results. To alleviate these problems, we propose a client-based architecture that incorporates noun phrasing and self-organizing map techniques. Two systems, namely CI Spider and Meta Spider, have been built based on this architecture. User evaluation studies have been conducted and the findings suggest that the proposed architecture can effectively facilitate web search and analysis.

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Comparing noun phrasing techniques for use with medical digital library tools

Cited by 77 publications

References 20 publications

Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

Document keyphrases as subject metadata: incorporating document key concepts in search results

Personalized spiders for web search and analysis

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