Improving retrieval of short texts through document expansion

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Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “g document representations. Another obvious approach to bridge the gap between query and document terms is to enrich document representations. This strategy works well for short texts such as tweets [Efron et al., 2012] and noisy transcriptions of speech [Singhal and Pereira, 1999]. Although not as ”
Document Expansion by Query Prediction (2019, methodology): “In this work, we adopt the alternative approach of enriching document representations (Tao et al., 2006; Pickens et al., 2010; Efron et al., 2012), which works particularly well for speech (Sing-hal and Pereira, 1999) and multi-lingual retrieval, where terms are noisy.”
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory (2024, unknown): “…both the highlighted information from compression and the completeness of the original value, we applied a simple document expansion technique (Tao et al., 2006; Efron et al., 2012), where the compressed information is concatenated with the original value to form the key during indexing 4 .”
Keyphrase Generation for Scientific Document Retrieval (2020, result): “Our work is similar in nature to previous research on document expansion (Tao et al., 2006; Efron et al., 2012), and is closely related to recent work on document expansion using automatically generated queries (Nogueira et al., 2019).”