Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries

Abstract

This paper presents a procedure to retrieve subsets of rele- vant documents from large text collections for Content Analysis, e.g. in social sciences. Document retrieval for this purpose needs to take account of the fact that analysts often cannot describe their research objective with a small set of key terms, especially when dealing with theoretical or rather abstract research interests. Instead, it is much easier to de ne a set of paradigmatic documents which re ect topics of interest as well as tar- geted manner of speech. Thus, in contrast to classic information retrieval tasks we employ manually compiled collections of reference documents to compose large queries of several hundred key terms, called dictionar- ies. We extract dictionaries via Topic Models and also use co-occurrence data from reference collections. Evaluations show that the procedure im- proves retrieval results for this purpose compared to alternative methods of key term extraction as well as neglecting co-occurrence data.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.