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A Neural Passage Model for Ad-hoc Document Retrieval
- Source :
- Lecture Notes in Computer Science ISBN: 9783319769400
- Publication Year :
- 2018
- Publisher :
- Springer International Publishing, 2018.
-
Abstract
- Traditional statistical retrieval models often treat each document as a whole. In many cases, however, a document is relevant to a query only because a small part of it contain the targeted information. In this work, we propose a neural passage model (NPM) that uses passage-level information to improve the performance of ad-hoc retrieval. Instead of using a single window to extract passages, our model automatically learns to weight passages with different granularities in the training process. We show that the passage-based document ranking paradigm from previous studies can be directly derived from our neural framework. Also, our experiments on a TREC collection showed that the NPM can significantly outperform the existing passage-based retrieval models.
- Subjects :
- Information retrieval
Process (engineering)
Computer science
020204 information systems
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
0202 electrical engineering, electronic engineering, information engineering
Window (computing)
020201 artificial intelligence & image processing
02 engineering and technology
Document retrieval
Ranking (information retrieval)
Subjects
Details
- ISBN :
- 978-3-319-76940-0
- ISBNs :
- 9783319769400
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783319769400
- Accession number :
- edsair.doi...........c753044edd794bb4bce4f0d756ce820f