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Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.


Papers
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Proceedings ArticleDOI
06 Nov 2017
TL;DR: Zhang et al. as mentioned in this paper proposed a novel name disambiguation method which leverages only relational data in the form of anonymized graphs and used a novel representation learning model to embed each document in a low dimensional vector space.
Abstract: In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.

97 citations

Journal ArticleDOI
Howard R. Turtle1
TL;DR: An introduction to text retrieval is provided and the main research related to the retrieval of legal materials is surveyed.
Abstract: The ability to find relevant materials in large document collections is a fundamental component of legal research. The emergence of large machine-readable collections of legal materials has stimulated research aimed at improving the quality of the tools used to access these collections. Important research has been conducted within the traditional information retrieval, the artificial intelligence, and the legal communities with varying degrees of interaction between these groups. This article provides an introduction to text retrieval and surveys the main research related to the retrieval of legal materials.

96 citations

Journal ArticleDOI
TL;DR: Although seemingly meaningful clusters can be obtained, the results indicate that the effort involved in finding clusters and adding the clustered terms to queries is far too great to warrant their use in an operational system.

96 citations

Journal ArticleDOI
TL;DR: This work combines detection of noun phrases with the use of WordNet as background knowledge to explore better ways of representing documents semantically for clustering, and finds that noun phrase analysis improves the WordNet-based clustering method.

96 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202239
2021107
2020130
2019144
2018111