Subtopic structuring for full-length document access
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Passage-level evidence in document retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient retrieval of partial documents
TREC-2 Proceedings of the second conference on Text retrieval conference
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Adaptive Parallel Sentences Mining from Web Bilingual News Collection
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Similarity measures for tracking information flow
Proceedings of the 14th ACM international conference on Information and knowledge management
A translation model for sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Discriminative probabilistic models for passage based retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Positional language models for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Conceptual recommender system for CiteSeerX
Proceedings of the third ACM conference on Recommender systems
Context-aware citation recommendation
Proceedings of the 19th international conference on World wide web
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Clickthrough-based translation models for web search: from word models to phrase models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Text segmentation: A topic modeling perspective
Information Processing and Management: an International Journal
A source independent framework for research paper recommendation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Recommending citations with translation model
Proceedings of the 20th ACM international conference on Information and knowledge management
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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The goal of a citation recommendation system is to suggest some references for a snippet in an article or a book, and this is very useful for both authors and the readers. The citation recommendation problem can be cast as an information retrieval problem, in which the query is the snippet from an article, and the relevant documents are the cited articles. In reality, the citation snippet and the cited articles may be described in different terms, and this makes the citation recommendation task difficult. Translation model is very useful in bridging the vocabulary gap between queries and documents in information retrieval. It can be trained on a collection of query and document pairs, which are assumed to be parallel. However, such training data contains much noise: a relevant document usually contains some relevant parts along with irrelevant ones. In particular, the citation snippet may only mention only some parts of the cited article's content. To cope with this problem, in this paper, we propose a method to train translation models on such noisy data, called position-aligned translation model. This model tries to align the query to the most relevant parts of the document, so that the estimated translation probabilities could rely more on them. We test this model in a citation recommendation task for scientific papers. Our experiments show that the proposed method can significantly improve the previous retrieval methods based on translation models.