Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus

Abstract

A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, which integrates features from two closely related fields: Terminology Extraction and Query Performance Prediction (QPP). Our method further expands modern candidate terms with ancient related terms, before assessing their corpus relevancy with QPP measures. We evaluate the empirical benefit of our method for a thesaurus for a diachronic Jewish corpus.

JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection

“…We plan to investigate additional aggregation methods and explore the impact of the individual models on the combined system to improve our system results. We also plan to try our system on other languages of different families, such as Semitic languages (Liebeskind and Liebeskind, 2020) and use LSC models to construct diachronic thesaurus, which bridges the lexical gap between modern and ancient language (Zohar et al, 2013;Liebeskind and Dagan, 2015;Liebeskind et al, 2016;Liebeskind et al, 2019).…”

Deep Learning for Period Classification of Historical Hebrew Texts

“…Responsa documents present various arguments by citing earlier sources, such as the Talmud and its commentators, legal codes, and earlier responses [Koppel, 2011]. Our corpus, used for previous IR and NLP research [Choueka, 1972, Fraenkel, 1976, Choueka et al, 1987, HaCohen-Kerner et al, 2008, Koppel, 2011, Zohar et al, 2013, Liebeskind and Dagan, 2015, contains 76,710 articles and approximately 100 million word tokens. Koppel [2011] emphasized another characteristic of Responsa, Responsa corpus was intended as a source of information and not a source of language use.…”