Exploring intrinsic information content models for addressing the issues of traditional semantic measures to evaluate verb similarity. (January 2022)

• Record Type: Journal Article
• Authors: Krishna Siva Prasad, M.; Sharma, Poonam
• Is Part Of: Computer speech & language. Volume 71(2022)
• Publication Date: 2022-01
• Subjects: Semantic similarity -- Intrinsic information content model -- Information content -- Path length -- Depth; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101280
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Semantic similarity measures play an important role in many natural language processing and information retrieval activities. It is highly challenging to measure semantic similarity with higher accuracy. A notable branch of semantic similarity evaluation based on information content (IC) is popular in this aspect. Intrinsic information content (IIC) models are another wing of IC based evaluation. Both IC based and IIC based approaches majorly handled similarity evaluation of nouns. Research related to semantic similarity assessment of verb pairs are rarely discussed. To bridge this gap, this work examines various IC based, IIC based approaches on verb pairs. A detailed discussion of the existing measures and their drawbacks are mentioned in this work. Strategies based on information content, length and depth of the concepts are discussed and tested on benchmark datasets. Existing intrinsic information content models are enhanced by addressing various issues like (a) dealing concepts with no path in WordNet and (b) handling the synonym sets of verb concepts. Measures based on path length, intrinsic information content, combined strategies and non-linear strategies for verb pairs are thoroughly inspected. This paper also presents novel strategies to understand novel aspects that are not addressed before. The strategies are experimented by generating the synonym sets of required parts-of-speech which proved very effective in improving the correlation with human judgment. Results on benchmark datasets specify that the proposed approaches for verb similarity will be a guiding factor for understanding the natural language processing tasks. Highlights: Semantic similarity measures to address the verb pairs. Path-based, information content based, combined and non-linear strategies for evaluating semantic similarity. Intrinsic information content models for evaluating semantic similarity. In this paper, handling the word pairs which does not have path in the semantic network proved effective. Generating proper synonym sets of the concepts, improved the correlation of the strategies proposed.