Finds terms most similar to the query terms labeled positive, and most dissimilar to those labeled negative. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. The terms' complex numerical descriptions are then simplified into the three most salient features. These features are plotted in a 3D space to illustrate both groupings as well as the strength of relationships. Queried terms can be artificially included in the visualization using the 'Show' parameter. Note that some queried terms will appear without being artificially included. Clicking on plotted terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.

Finds terms most similar to the query terms labeled positive, and most dissimilar to those labeled negative. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. Utilizing the results of this first search, a second search identifies terms similar not only to the initial query, but to the entire set of terms in the first result, extending the exploration to include additional, abstract, or indirectly related terms. Clicking on terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.

Constructs a hypergraph by first generating an initial list of terms most similar to the query terms labeled positive, and most dissimilar to those labeled negative. Each term from this initial list is then used to produce additional lists of similar terms, with overlapping terms being used to connect the groups within the hypergraph. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. Raising the threshold ensures a more closely related set of terms, while lowering the threshold broadens the diversity within the groups. Clicking on plotted terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.

Builds a network (graph) of related terms by finding terms most similar to the query terms labeled positive, and most dissimilar to those labeled negative. Each term from this initial list is then used to produce additional lists of similar terms. Lists of similar terms are then generated for each term in the secondary set of lists. Relationships are illustrated with red lines in the first iteration, green lines in the second iteration, and blue lines in the third. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. Raising the threshold ensures a more closely related set of terms, while lowering the threshold broadens the diversity of the depicted concepts. Clicking on plotted terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.

Finds terms most similar to the query terms labeled positive, and most dissimilar to those labeled negative. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. The relationships between the query and its most closely related terms are analyzed, identifying those with the most and least influence on these relationships to produce tables of positive and negative correlations. Queried terms can be artificially included in the calculation using the 'Show' parameter. Note that some queried terms will appear without being artificially included, and some queried terms may not appear in the result even if artificially included. Clicking on terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.

Suggests OCM subject identifiers for user-submitted text based on the similarity of the vocabulary to the eHRAF World Cultures corpus, ranking the proposed subjects by the probability they are associated with the words of the user-submitted text.

Suggests subtopics for user-submitted text based on similarity to the vocabulary associated with a specific OCM identifier, providing two examples per subtopic identified. Examples include both a list of salient terms for the topic as well as matching paragraphs from the corpus. Where possible, specific names and numbers within lists of salient terms have been substituted for generic (named-entity recognition) labels.

Identifies dimensions of a query by first generating an initial list of terms most similar to the those labeled positive, and most dissimilar to those labeled negative. Each term from this initial list is then used to produce an additional list of similar terms: a dimension. Combinations of the dimensions are explored by subtracting the dimensions from one another to reveal contrasting features. Each query term's relevance can be adjusted using the weight, where 1/-1 indicates a neutral value. Raising the threshold ensures a more closely related set of terms, while lowering the threshold broadens the diversity within the dimensions. The 'Top Number' parameter sets a maximum number of terms per dimension. Clicking on terms performs a search in eHRAF World Cultures. Using an indexer improves the speed of the computation, but lowers its accuracy.