TCS: a shell for content-based text categorization

Hayes, Philip J.; Andersen, Peggy M.; Nirenburg, Irene; Schmandt, Linda M.

doi:10.1109/caia.1990.89206

Cited by 78 publications

(49 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, information management tools force a classification on users, either by automatically classifying objects, as in text categorisation systems [Hayes et al, 1990], or forcing users to classify objects, usually in some form of hierarchical system [Malone 1983]. For example, photographs and music are generally organised in albums and possibly further sub-categorised by artist, date, genre etc.…”

Section: Introductionmentioning

confidence: 99%

Towards memory supporting personal information management tools

Elsweiler

Ruthven

Jones

2007

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

In this article we discuss re-retrieving personal information objects and relate the task to recovering from lapse(s) in memory. We propose that fundamentally it is lapses in memory that impede users from successfully re-finding the information they need. Our hypothesis is that by learning more about memory lapses in non-computing contexts and how people cope and recover from these lapses, we can better inform the design of PIM tools and improve the user's ability to re-access and re-use objects. We describe a diary study that investigates the everyday memory problems of 25 people from a wide range of backgrounds. Based on the findings, we present a series of principles that we hypothesize will improve the design of personal information management tools. This hypothesis is validated by an evaluation of a tool for managing personal photographs, which was designed with respect to our findings. The evaluation suggests that users' performance when re-finding objects can be improved by building personal information management tools to support characteristics of human memory.

show abstract

Section: Introductionmentioning

confidence: 99%

Towards memory supporting personal information management tools

Elsweiler

Ruthven

Jones

2007

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The result of this is a content-based filtering system selects information items on the basis of correlation between the content of the items and the preferences of the users as contrast to a collaborative filtering system that selects items based on the correlation between people with similar preferences [6,7].Electronic mails were the earlier domain for the work on information filtering ,subsequent papers documented diversified domains including the newswire articles, Internet "news "articles and broader network resources [8][9][10].Mostly the Documents processed in content-based filtering are textual in nature and thus makes content-based filtering near to text classification. Filtering process can be modeled as a binary classification, single label, partitioning incoming documents into relevant and non-relevant categories [11]. In enhancement some complex filtering systems includes multi-label text categorization in which automatically labeling of messages into partial thematic categories is done.…”

Section: Content-based Filteringmentioning

confidence: 99%

A Survey on OSN Message Filtering

Gandhi¹,

Panditrao²,

Lahane³

2015

IJCA

View full text Add to dashboard Cite

In the specified documents data mining technique has been deprecated for filtering the OSN wall with unwanted messages or any type of vulgar messages. OSN is Online Social Network which has become an important part of the people life these days. People communicate over it with friends, relatives over a OSN wall. Thus to provide a feel of security to users personal stuff it is important to filter the OSN wall for any unwanted message .But the questions Arises, how to filter the OSN wall with a facility provided of blocking unwanted messages on the user's private wall. This can be gained through the flexible rule-based system which implements filtering criteria that can be customized by the user and a Machine Learning-based soft classifier which automatically labels messages in the support of content-based filtering .This paper consist of a literature survey paper of the existing system with proposed system as a technique to filter similar meaning words using Ontology along with the basic functionality to filter the OSN wall for unwanted message. In this paper a technique to build a social network with filtered message is elaborated. General TermsOn-line Social Network, short text classifier.

show abstract

“…or ODP). Rule-based or knowledge engineering systems allow users to construct classification rules [13] for documents. Commercial knowledge management systems such as DataHarmony (dataharmony.com) support both automated rules and manual assignment.…”

Section: Offline Fast-feature Techniquesmentioning

confidence: 99%

Categorizing web search results into meaningful and stable categories using fast-feature techniques

Kules

Kustanowitz

Shneiderman

2006

Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries

View full text Add to dashboard Cite

When search results against digital libraries and web resources have limited metadata, augmenting them with meaningful and stable category information can enable better overviews and support user exploration. This paper proposes six "fast-feature" techniques that use only features available in the search result list, such as title, snippet, and URL, to categorize results into meaningful categories. They use credible knowledge resources, including a US government organizational hierarchy, a thematic hierarchy from the Open Directory Project (ODP) web directory, and personal browse histories, to add valuable metadata to search results. In three tests the percent of results categorized for five representative queries was high enough to suggest practical benefits: general web search (76-90%), government web search (39-100%), and the Bureau of Labor Statistics website (48-94%). An additional test submitted 250 TREC queries to a search engine and successfully categorized 66% of the top 100 using the ODP and 61% of the top 350. Fast-feature techniques have been implemented in a prototype search engine. We propose research directions to improve categorization rates and make suggestions about how web site designers could re-organize their sites to support fast categorization of search results.

show abstract

TCS: a shell for content-based text categorization

Cited by 78 publications

References 8 publications

Towards memory supporting personal information management tools

Towards memory supporting personal information management tools

A Survey on OSN Message Filtering

Categorizing web search results into meaningful and stable categories using fast-feature techniques

Contact Info

Product

Resources

About