An empirical study of query expansion and cluster-based retrieval in language modeling approach

2007 ◽  
Vol 43 (2) ◽  
pp. 302-314 ◽  
Author(s):  
Seung-Hoon Na ◽  
In-Su Kang ◽  
Ji-Eun Roh ◽  
Jong-Hyeok Lee
Keyword(s):  
Empirical Study ◽  
Query Expansion ◽  
Language Modeling ◽  
Modeling Approach

A Language Modeling Approach to Information Retrieval

2017 ◽  
Vol 51 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Jay M. Ponte ◽  
W. Bruce Croft
Keyword(s):  
Information Retrieval ◽  
Language Modeling ◽  
Modeling Approach

scholarly journals On Building an Interpretable Topic Modeling Approach for the Urdu Language

2020 ◽  
Author(s):  
Zarmeen Nasim
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Topic Modeling ◽  
Language Modeling ◽  
Low Resource ◽  
Modeling Approach ◽  
Part Of Speech ◽  
Modeling Techniques ◽  
Pos Tagger

This research is an endeavor to combine deep-learning-based language modeling with classical topic modeling techniques to produce interpretable topics for a given set of documents in Urdu, a low resource language. The existing topic modeling techniques produce a collection of words, often un-interpretable, as suggested topics without integrat-ing them into a semantically correct phrase/sentence. The proposed approach would first build an accurate Part of Speech (POS) tagger for the Urdu Language using a publicly available corpus of many million sentences. Using semanti-cally rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clus-tering and topic modeling techniques to produce a list of potential topics for a given set of documents. Finally, this list of topics would be sent to a labeler module to produce syntactically correct phrases that will represent interpretable topics.

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