An Attention-based Deep Relevance Model for Few-shot Document Filtering

2021 ◽  
Vol 39 (1) ◽  
pp. 1-35
Author(s):  
Bulou Liu ◽  
Chenliang Li ◽  
Wei Zhou ◽  
Feng Ji ◽  
Yu Duan ◽  
...  
Keyword(s):  
Document Filtering ◽  
Relevance Model

scholarly journals A Deep Relevance Model for Zero-Shot Document Filtering

2018 ◽  
Author(s):  
Chenliang Li ◽  
Wei Zhou ◽  
Feng Ji ◽  
Yu Duan ◽  
Haiqing Chen
Keyword(s):  
Document Filtering ◽  
Relevance Model

A relevance model for a data warehouse contextualized with documents

2009 ◽  
Vol 45 (3) ◽  
pp. 356-367 ◽  
Author(s):  
Juan Manuel Pérez ◽  
Rafael Berlanga ◽  
María José Aramburu
Keyword(s):  
Data Warehouse ◽  
Relevance Model

scholarly journals Seed-Guided Topic Model for Document Filtering and Classification

2019 ◽  
Vol 37 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Chenliang Li ◽  
Shiqian Chen ◽  
Jian Xing ◽  
Aixin Sun ◽  
Zongyang Ma
Keyword(s):  
Topic Model ◽  
Document Filtering

scholarly journals Understanding an enriched multidimensional user relevance model by analyzing query logs

2017 ◽  
Vol 68 (12) ◽  
pp. 2743-2754 ◽  
Author(s):  
Jingfei Li ◽  
Peng Zhang ◽  
Dawei Song ◽  
Yue Wu
Keyword(s):  
Query Logs ◽  
Relevance Model

User Profiles for Personalizing Digital Libraries

2009 ◽  
pp. 149-158 ◽  
Author(s):  
Giovanni Semeraro ◽  
Pierpaolo Basile ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Machine Learning ◽  
Digital Libraries ◽  
Machine Learning Techniques ◽  
Linguistic Knowledge ◽  
User Profiles ◽  
User Interests ◽  
Learning Techniques ◽  
Document Filtering ◽  
The Individual ◽  
Machine Readable

Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.

A context-dependent relevance model

2015 ◽  
Vol 67 (3) ◽  
pp. 582-593 ◽  
Author(s):  
Edward Kai Fung Dang ◽  
Robert W.P. Luk ◽  
James Allan
Keyword(s):  
Context Dependent ◽  
Relevance Model
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