Descriptor
| Algorithms | 4 |
| Documentation | 4 |
| Automatic Indexing | 3 |
| Classification | 3 |
| Databases | 3 |
| Bayesian Statistics | 2 |
| Cluster Grouping | 2 |
| Information Retrieval | 2 |
| Mathematical Formulas | 2 |
| Mathematical Models | 2 |
| Probability | 2 |
| More ▼ | |
Publication Type
| Reports - Research | 4 |
| Journal Articles | 2 |
| Opinion Papers | 2 |
| Reports - Descriptive | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewedBartell, Brian T.; And Others – Journal of the American Society for Information Science, 1995
Discussion of the failure of individual keywords to identify conceptual content of documents in retrieval systems highlights Metric Similarity Modeling, a method for creating vector space representation of documents based on modeling target interdocument similarity values. Semantic relatedness, latent semantic indexing, an indexing and retrieval…
Descriptors: Algorithms, Databases, Documentation, Indexing
PDF pending restorationWhite, Lee J.; And Others – 1975
The major advantage of sequential classification, a technique for automatically classifying documents into previously selected categories, is that the entire document need not be processed before it is classified. This method assumes the availability of a priori categories, a selection of keywords representative of these categories, and the a…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification
Peer reviewedWillett, Peter – Information Processing and Management, 1981
Describes a fast algorithm for comparing the lists of terms representing documents in automatic classification experiments. Complexity and running time for the algorithm are compared to other procedures, and a short algol-like routine is presented in the appendix. Eight references are included. (Author/BK)
Descriptors: Algorithms, Automatic Indexing, Classification, Documentation
Kar, B. Gautam; White, Lee J. – 1975
The feasibility of using a distance measure, called the Bayesian distance, for automatic sequential document classification was studied. Results indicate that, by observing the variation of this distance measure as keywords are extracted sequentially from a document, the occurrence of noisy keywords may be detected. This property of the distance…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification


