Papers › Deeper Text Understanding for IR with Contextual Neural Language Modeling
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Zhuyun Dai, Jamie Callan
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the text content of a query or a document. This paper studies leveraging a recently-proposed contextual neural language model, BERT, to provide deeper text understanding for IR. Experimental results demonstrate that the contextual text representations from BERT are more effective than traditional word embeddings. Compared to bag-of-words retrieval models, the contextual language model can better leverage language structures, bringing large improvements on queries written in natural languages. Combining the text understanding ability with search knowledge leads to an enhanced pre-trained BERT model that can benefit related search tasks where training data are limited.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Ad-Hoc Information Retrieval | TREC Robust04 | BERT-MaxP | nDCG@20 | 0.469 | #5 of 21 | Archive leaderboard | report |
| Ad-Hoc Information Retrieval | TREC Robust04 | BERT-SumP | nDCG@20 | 0.467 | #6 of 21 | Archive leaderboard | report |
| Ad-Hoc Information Retrieval | TREC Robust04 | BERT-FirstP | nDCG@20 | 0.444 | #11 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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