The CQC Algorithm: Cycling in Graphs to Semantically Enrich and Enhance a Bilingual Dictionary

TL;DR

The paper introduces the CQC algorithm that uses graph cycles to disambiguate translations, improve bilingual dictionary quality, and extract synonyms, enhancing the resource's semantic richness.

Contribution

It presents a novel graph-based algorithm for disambiguating translations and improving bilingual dictionaries, also applicable to synonym extraction.

Findings

Effective disambiguation of ambiguous translations.

Improved dictionary quality through structural corrections.

Successful application to synonym extraction.

Abstract

Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information, such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is represented as a graph, and cyclic patterns are sought in the graph to assign an appropriate sense tag to each translation in a lexical entry. Further, we use the algorithms output to improve the quality of the dictionary itself, by suggesting accurate solutions to structural problems such as misalignments, partial alignments and missing entries. Finally, we successfully apply CQC to the task of synonym extraction.