Primal Meaning Recommendation via On-line Encyclopedia

TL;DR

This paper presents a hybrid model to automatically identify the primal meaning of expressions from online encyclopedia data, improving accuracy in sense importance recognition for knowledge base construction.

Contribution

It introduces a novel hybrid approach combining pattern recognition and relationship analysis, with weakly supervised and unsupervised learning, to recommend primal meanings effectively.

Findings

Achieved P@1 of 83.3% and MAP of 90.5%, outperforming baseline methods.

Demonstrated effectiveness of hybrid model in sense importance detection.

Enhanced knowledge base construction by identifying primal meanings more accurately.

Abstract

Polysemy is a very common phenomenon in modern languages. Under many circumstances, there exists a primal meaning for the expression. We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced. Many of the new appearing meanings of the expressions are either originated from a primal meaning, or are merely literal references to the original expression, e.g., apple (fruit), Apple (Inc), and Apple (movie). When constructing a knowledge base from on-line encyclopedia data, it would be more efficient to be aware of the information about the importance of the senses. In this paper, we would like to explore a way to automatically recommend the primal meaning of an expression based on the textual descriptions of the multiple senses of an expression from on-line encyclopedia websites. We propose a hybrid model that captures both the pattern of the description and the relationship between different descriptions with both weakly supervised and unsupervised models. The experiment results show that our method yields a good result with a P@1 (precision) score of 83.3 per cent, and a MAP (mean average precision) of 90.5 per cent, surpassing the UMFS-WE baseline by a big margin (P@1 is 61.1 per cent and MAP is 76.3 per cent).