The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Learning to Match using Local and Distributed Representations of Text for Web Search
Open accessAbstract
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favourable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or 'duet' performs significantly better than either neural network individually on a Web page ranking task, and significantly outperforms traditional baselines and other recently proposed models based on neural networks.
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Distributed Representations of Words and Phrases and their Compositionality
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Indexing by latent semantic analysis
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Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Cumulated gain-based evaluation of IR techniques
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Probabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
Probabilistic latent semantic indexing
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A Language Modeling Approach to Information Retrieval
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Cited by
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301
Nogueira, R., & Cho, K. (2019) · arXiv · doi:10.48550/arxiv.1901.04085 · Cited by 615
Document Ranking with a Pretrained Sequence-to-Sequence Model
Nogueira, R., Jiang, Z., Pradeep, R., & Lin, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.63 · Cited by 448
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
Dai, Z., Xiong, C., Callan, J., & Liu, Z. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159659 · Cited by 341
Neural Ranking Models with Weak Supervision
Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080832 · Cited by 299
Metadata
- Authors
- Mitra, B., Diaz, F., & Craswell, N.
- Venue
- Proceedings of the 26th International Conference on World Wide Web
- Published
- 2017-04-03
- Pages
- 1291-1299
- OpenAlex
- W2539671052
- Cited by
- 426
- References
- 40
- FWCI
- 50.19
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Mitra, B., Diaz, F., & Craswell, N. (2017). Learning to Match using Local and Distributed Representations of Text for Web Search. Proceedings of the 26th International Conference on World Wide Web. https://doi.org/10.1145/3038912.3052579
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