BLOSSOM: Best path Length on a Semantic Self-Organizing Map
نویسندگان
چکیده
We describe Vector Generation from Explicitly-defined Multidimensional semantic Space (VGEM), a method for converting a measure of semantic relatedness (MSR) into vector form. We also describe Best path Length on a Semantic Self-Organizing Map (BLOSSOM), a semantic relatedness technique employing VGEM and a connectionist, nonlinear dimensionality reduction technique. The psychological validity of BLOSSOM is evaluated using test cases from a large free-association norms dataset; we find that BLOSSOM consistently shows improvement over VGEM. BLOSSOM matches the performance of its base-MSR using a 21 dimensional vector-space and shows promise to outperform its base-MSR with a more rigorous exploration of the parameter space. In addition, BLOSSOM provides benefits such as document relatedness, concept-path formation, intuitive visualizations, and unsupervised text clustering.
منابع مشابه
Application of growing self-organizing map to small-world networking
This paper studies a novel application of growing self-organizing maps to networking. In our algorithm nodes for the networking are applied successively as input data. Adapting to the input, the map can grow and can change the topology. Performing basic numerical experiments, we have confirmed that our algorithm can generate small-world like networks characterized by relatively small average pa...
متن کاملSelf-organizing Map Analysis of Conceptual and Semantic Relations for Noun
In this paper, we analyzed self-organizing map of conceptual and semantic relations for noun, discussing the semantic distinction between conceptual nouns for natural language processing and syntax acquisition, summarizing the lexical meaning and a detailed description of semantic lexical tagging of nouns. Our result reflects the noun-attribute associations and focuses on the conceptual relatio...
متن کاملUncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm
Group tourism is one of the most important tasks in tourist recommender systems. These systems, despite of the potential contradictions among the group's tastes, seek to provide joint suggestions to all members of the group, and propose recommendations that would allow the satisfaction of a group of users rather than individual user satisfaction. Another issue that has received less attention i...
متن کاملUnsupervised Text Classification and Search using Word Embeddings on a Self-Organizing Map
This paper presents the results of an experimental implementation of a document classifier leveraging contextual word embeddings clustered on a self-organizing map. The problem of document categorization is further compounded when there are no predefined categories, or conversely there are too many categories, that documents may be bucketed into. This paper proposes to address these problems by...
متن کاملEmergence of Linguistic Representations by Independent Component Analysis
Our aim is to find syntactic and semantic relationships and roles of words based on the analysis of corpora. We study three methods for analyzing words in contexts as potential methods for solving this task. The methods are latent semantic analysis, self-organizing map and independent component analysis. Latent semantic analysis is a simple method for automatic generation of concepts that are u...
متن کامل