Software Framework for Topic Modelling with Large Corpora

@inproceedings{884893,
   author = {Řehůřek, Radim and Sojka, Petr},
   address = {Valletta, Malta},
   booktitle = {Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks},
   keywords = {document similarity; NLP; software; vector space model; topical modelling; software framework; topical document similarity; Python; IR; LSA; LDA; gensim; DML-CZ},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {Valletta, Malta},
   isbn = {2-9517408-6-7},
   pages = {46--50},
   publisher = {University of Malta},
   title = {Software Framework for Topic Modelling with Large Corpora},
   url = {http://www.lrec-conf.org/proceedings/lrec2010/workshops/W10.pdf},
   year = {2010}
}

TY  - JOUR
ID  - 884893
AU  - Řehůřek, Radim - Sojka, Petr
PY  - 2010
TI  - Software Framework for Topic Modelling with Large Corpora
PB  - University of Malta
CY  - Valletta, Malta
SN  - 2951740867
KW  - document similarity
KW  - NLP
KW  - software
KW  - vector space model
KW  - topical modelling
KW  - software framework
KW  - topical document similarity
KW  - Python
KW  - IR
KW  - LSA
KW  - LDA
KW  - gensim
KW  - DML-CZ
UR  - http://www.lrec-conf.org/proceedings/lrec2010/workshops/W10.pdf
L2  - http://www.fi.muni.cz/usr/sojka/papers/lrec2010-rehurek-sojka.pdf
N2  - Large corpora are ubiquitous in today's world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). We identify gap in existing VSM implementations, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. In this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.
ER  - 

ŘEHŮŘEK, Radim a Petr SOJKA. Software Framework for Topic Modelling with Large Corpora. In \textit{Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks}. Valletta, Malta: University of Malta. s.~46--50. ISBN~2-9517408-6-7. 2010.