Bilingual Terminology Extraction Using Multi-level Termhood

TL;DR

This paper introduces a multi-level termhood approach for improved monolingual terminology extraction and bilingual term alignment, enhancing performance in cross-language applications like machine translation and information retrieval.

Contribution

It proposes a novel multi-level termhood method that considers term and sentence distributions to improve terminology extraction and bilingual alignment accuracy.

Findings

Multi-level termhood outperforms existing methods in terminology extraction.

Using termhood as a constraint improves bilingual term alignment.

Experimental results demonstrate enhanced performance in both tasks.

Abstract

Purpose: Terminology is the set of technical words or expressions used in specific contexts, which denotes the core concept in a formal discipline and is usually applied in the fields of machine translation, information retrieval, information extraction and text categorization, etc. Bilingual terminology extraction plays an important role in the application of bilingual dictionary compilation, bilingual Ontology construction, machine translation and cross-language information retrieval etc. This paper addresses the issues of monolingual terminology extraction and bilingual term alignment based on multi-level termhood. Design/methodology/approach: A method based on multi-level termhood is proposed. The new method computes the termhood of the terminology candidate as well as the sentence that includes the terminology by the comparison of the corpus. Since terminologies and general words usually have differently distribution in the corpus, termhood can also be used to constrain and enhance the performance of term alignment when aligning bilingual terms on the parallel corpus. In this paper, bilingual term alignment based on termhood constraints is presented. Findings: Experiment results show multi-level termhood can get better performance than existing method for terminology extraction. If termhood is used as constrain factor, the performance of bilingual term alignment can be improved.