Phenomenological semantic distance from latent dirichlet allocations (LDA) classification

#The present application relates generally to a system and method that can be used to classify and cluster subjects based on their relationships with other subjects.

#There are many application for learning from unstructured natural language. However, the rapid increase in data production creates difficulties in understanding such a large amount of data. Previous attempts have been made to classify the number of subject matters that exist on the internet, with one such attempt classifying over 2000 different subjects, including general categories like history, cars, travel, and hotels, to much more specific subjects like renaissance dance or bed bugs.

#It would be useful to have the ability to calculate semantic distances between these subjects in order to determine whether subjects are “close” to any given subject, and provide a quantitative measurement of that closeness. Such a measurement can be used to provide further information during a query about a particular subject by collecting information from the related subjects, or to find subject clusters within a pre-determined list of all subjects.

#The foregoing and other aspects of the present invention are best understood from the following detailed description when read in connection with the accompanying drawings. For the purpose of illustrating the invention, there is shown in the drawings embodiments that are presently preferred, it being understood, however, that the invention is not limited to the specific instrumentalities disclosed. Included in the drawings are the following Figures:

#FIG. 1 illustrates a block diagram of the functionality of one illustrative embodiment of a semantic distance calculation system;

#FIG. 2 depicts an example classification list used by a semantic distance calculation system, according to embodiments described herein;

#The present description and claims may make use of the terms “a,” “at least one of,” and “one or more of,” with regard to particular features and elements of the illustrative embodiments. It should be appreciated that these terms and phrases are intended to state that there is at least one of the particular feature or element present in the particular illustrative embodiment, but that more than one can also be present. That is, these terms/phrases are not intended to limit the description or claims to a single feature/element being present or require that a plurality of such features/elements be present. To the contrary, these terms/phrases only require at least a single feature/element with the possibility of a plurality of such features/elements being within in the scope of the description and claims.

#In addition, it should be appreciated that the following description uses a plurality of various examples for various elements of the illustrative embodiments to further illustrate example implementations of the illustrative embodiments and to aid in the understanding of the mechanisms of the illustrative embodiments. These examples are intended to be non-limiting and are not exhaustive of the various possibilities for implementing the mechanisms of the illustrative embodiments. It will be apparent to those of ordinary skill in the art in view of the present description that there are many other alternative implementations for these various elements that may be utilized in addition to, or in replacement of, the example provided herein without departing from the spirit and scope of the present invention.

#The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.