Identifying missing dictionary entries with frequency-conserving context models

TL;DR

This paper introduces a frequency-conserving context model to identify missing dictionary entries by analyzing phrase data, enhancing lexical coverage through collaborative filtering and prediction.

Contribution

The work presents a novel frequency-conserving phrase model applied to Wiktionary data, enabling effective detection of missing lexical entries for dictionary expansion.

Findings

Successfully identified numerous candidate missing entries

Developed a new lexical extraction technique

Enhanced dictionary coverage with minimal false positives

Abstract

In an effort to better understand meaning from natural language texts, we explore methods aimed at organizing lexical objects into contexts. A number of these methods for organization fall into a family defined by word ordering. Unlike demographic or spatial partitions of data, these collocation models are of special importance for their universal applicability. While we are interested here in text and have framed our treatment appropriately, our work is potentially applicable to other areas of research (e.g., speech, genomics, and mobility patterns) where one has ordered categorical data, (e.g., sounds, genes, and locations). Our approach focuses on the phrase (whether word or larger) as the primary meaning-bearing lexical unit and object of study. To do so, we employ our previously developed framework for generating word-conserving phrase-frequency data. Upon training our model with the Wiktionary---an extensive, online, collaborative, and open-source dictionary that contains over 100,000 phrasal-definitions---we develop highly effective filters for the identification of meaningful, missing phrase-entries. With our predictions we then engage the editorial community of the Wiktionary and propose short lists of potential missing entries for definition, developing a breakthrough, lexical extraction technique, and expanding our knowledge of the defined English lexicon of phrases.