Text Relatedness Based on a Word Thesaurus: An Expert Analysis
The paper "Text Relatedness Based on a Word Thesaurus," published in the Journal of Artificial Intelligence Research, presents an advanced computational method for estimating semantic relatedness between text segments utilizing a thesaurus, specifically WordNet. This work introduces a novel measure, termed Omiotis, designed to capture both lexical and semantic relatedness between text elements.
Key Contributions and Methodology
The core contribution of the paper is the development of Omiotis, a semantic relatedness measure that integrates two levels of semantic computation: word-to-word and text-to-text relatedness. Omiotis builds upon the semantic relatedness of individual words, utilizing a word thesaurus to establish implicit semantic links between them. The word-to-word semantic relatedness measure (SR) evaluates the semantic paths connecting word pairs by considering the length of these paths, the specificity of intermediate nodes, as reflected by their depth in WordNet's hierarchy, and the weights of semantic edges in the path.
Computational Approach
Semantic relatedness between word senses is computed through a modified Dijkstra's algorithm that identifies the path maximizing the product of edge weights. The SR measure achieves increased coverage and performance by leveraging all available parts of speech in WordNet and utilizing a comprehensive set of semantic relations rather than relying solely on hierarchical links.
For texts, Omiotis evaluates the lexical relevance using a harmonic mean of TF-IDF weights combined with the semantic relatedness of words, thereby determining the degree of semantic connectivity between texts. Omiotis computes the semantic relatedness between text fragments with a focus on integrating lexical similarity and semantic connectivity.
Experimental Evaluation
Omiotis was validated through rigorous experimental evaluation across diverse linguistic tasks. In word-to-word similarity assessments using benchmark datasets such as Rubenstein and Goodenough and Miller and Charles, the SR measure demonstrates superior correlation with human-judged relatedness scores compared to traditional lexicon-based, corpus-based, and hybrid methods.
In the synonym identification tasks using TOEFL and ESL data sets, SR displayed strong performances, surpassing several established methods. In addition, the paper reports competent results in Scholastic Aptitude Test analogy questions, showcasing the measure's capability to address nuanced semantic relationships. Furthermore, Omiotis exhibited promising results in sentence similarity tasks and paraphrase recognition using datasets such as the Microsoft Research Paraphrase Corpus, further highlighting its applicability in real-world text processing.
Theoretical and Practical Implications
The introduction of Omiotis potentially enhances various computational linguistics applications, including text classification, clustering, paraphrase recognition, and much more. Notably, its ability to integrate semantic relatedness at multiple granularity levels marks a step forward in text analysis methodologies, enabling more refined document retrieval, summarization, and understanding.
Future Prospects
While Omiotis showcases compelling results, future work entails refining its computational scalability and exploring its utility in broader NLP tasks such as cross-lingual information retrieval, document clustering, and query expansion. The integration of semantic relatedness in machine learning frameworks paves the way for enriched linguistic models and sophisticated analysis tools.
In summary, this paper presents a robust and comprehensive semantic relatedness measure, Omiotis, proving effective across linguistic tasks and offering promising directions for future research in semantic processing technologies. The use of a thesaurus-driven approach heralds potential improvements in interpreting and utilizing semantic information in computational systems.