From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing

27th ACM International Conference on Information and Knowledge Management (CIKM '18) 2018 Hamed ZamaniMostafa DehghaniW. Bruce CroftErik Learned-Millerand Jaap Kamps

The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with efficiency. Current neural ranking models are implemented as multistage rankers: for efficiency reasons, the neural model only re-ranks the top ranked documents retrieved by a first-stage efficient ranker in response to a given query... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Ad-Hoc Information Retrieval TREC Robust04 QL MAP 0.2499 # 12
Ad-Hoc Information Retrieval TREC Robust04 SNRM MAP 0.2856 # 5
[email protected] 0.3766 # 10
[email protected] 0.4310 # 11
Ad-Hoc Information Retrieval TREC Robust04 SNRM-PRF MAP 0.2971 # 3
[email protected] 0.3948 # 6
[email protected] 0.4391 # 9

Methods used in the Paper


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