Enhancement of Subjective Logic for Semantic Document Analysis Using Hierarchical Document Signature

In this paper, an extension of Subjective Logic (SL) is presented which uses semantic information from a document to find 'opinions' about a sentence. This method computes semantic overlap of events (words or sentences) using Hierarchical Document Signature (HDS) and uses it as evidence to formulate SL belief measures to order sentences according to their importance. Stronger the opinion, more is the significance. These significant sentences then form extractive summaries of the document. The experimental results show that summaries generated by this method are more similar to human generated ones have outperformed the baseline summaries on average over all the data sets considered

Fuzzy Word Similarity: A Semantic Approach Using WordNet

In this paper we present a hybrid measure of semantic word similarity using fuzzy inference system which combines both the corpus based distance measures as well as gloss overlap to get the final similarity between two words. We use WordNet as a lexical dictionary to get semantic information about words. We show that this new measure reasonably correlates to human judgments and the average performance is boosted by using triangular membership function in the output

Estimation of Possibility-Probability Distributions

We demonstrate a theory for evaluating the likelihood of a probability by way of possibility distributions. This theory derives from the standard probability distribution theory by using the possibility to define an arbitrary function whose values are bo

Complex Structured Decision Making Model: A hierarchical frame work for complex structured data

We introduce a hierarchical framework we call Complex Structured Decision Making model for complexly structured knowledge representation in intelligent decision making. We show that our model extends non-hierarchical (flat) decision making models to hierarchical decision making models that are similar to comprehensible human decision making processes. Further, we make an argument that hierarchial representation of knowledge in a Complex Structured Decision Making Model simplifies the approximation of aggregation functions to easily adapt to the underline relation of the system. Additionally, using a real world complex structured data set, we show that hierarchically organized Fuzzy Integrals, e.g. Choquet Integral, and Sugeno Integral and Fuzzy Signatures outperform these non-hierarchical Fuzzy Integrals

WRAO and OWA learning using Levenberg-Marquardt and genetic algorithms

The generalized Weighted Relevance Aggregation Operator (WRAO) is a non-additive aggregation function. The Ordered Weighted Aggregation Operator (OWA) (or its generalized form: Generalized Ordered Weighted Aggregation Operator (GOWA)) is more restricted with the additivity constraint in its weights. In addition, it has an extra weights reordering step making it hard to learn automatically from data. Our intension here is to compare the efficiency (or effectiveness) of learning these two types of aggregation functions from empirical data. We employed two methods to learn WRAO and GOWA: Levenberg-Marquardt (LM) and a Genetic Algorithm (GA) based method. We use UCI (University of California Irvine) benchmark data to compare the aggregation performance of non-additive WRAO and additive GOWA. We found that the non-constrained aggregation function WRAO was learnt well automatically and produced consistent results, while GOWA was learnt less well and quite inconsistently

Polymorphic Fuzzy Signatures

The fuzzy signature [1], [2] approach is aimed at finding a hierarchically decomposed solutions by adding new elements to Zadeh's approach [3]. It tackles the problem by splitting the problem into hierarchically organized local sub-models and by applyin

A Comparison: Fuzzy Signatures and Choquet Integral

Fuzzy Signatures are hierarchical multi aggregative descriptors of objects. They have reduced computational complexity compared to formal fuzzy rule based systems. Weighted Relevance Aggregation enhances the performance of hierarchical Fuzzy Signatures. Thus, they are very robust and flexible under perturbed input data. On the other hand the Choquet Integral, which is based on fuzzy measures, is a powerful aggregation tool in multi-criteria decision making. We compared Fuzzy Signatures and the Choquet Integral as practical applications for hierarchical and non-hierarchical data aggregation/organization methods

Improving Hierarchical Document Signature Performance by Classifier Combination

We present a classifier-combination experimental framework for part-of-speech (POS) tagging in which four different POS taggers are combined in order to get a better result for sentence similarity using Hierarchical Document Signature (HDS). It is important to abstract information available to form humanly accessible structures. The way people think and talk is hierarchical with limited information presented in any one sentence, and that information is always linked together to further information. As such, HDS is a significant way to represent sentences when finding their similarity. POS tagging plays an important role in HDS. But POS taggers available are not perfect in tagging words in a sentence and tend to tag words improperly if they are either not properly cased or do not match the corpus dataset by which these taggers are trained. Thus, different weighted voting strategies are used to overcome some of these drawbacks of these existing taggers. Comparisons between individual taggers and combined taggers under different voting strategies are made. Their results show that the combined taggers provide better results than the individual ones