@inproceedings{schwartz-etal-2017-dlatk,
title = "{DLATK}: Differential Language Analysis {T}ool{K}it",
author = "Schwartz, H. Andrew and
Giorgi, Salvatore and
Sap, Maarten and
Crutchley, Patrick and
Ungar, Lyle and
Eichstaedt, Johannes",
editor = "Specia, Lucia and
Post, Matt and
Paul, Michael",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-2010",
doi = "10.18653/v1/D17-2010",
pages = "55--60",
abstract = "We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.",
}
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<abstract>We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.</abstract>
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%0 Conference Proceedings
%T DLATK: Differential Language Analysis ToolKit
%A Schwartz, H. Andrew
%A Giorgi, Salvatore
%A Sap, Maarten
%A Crutchley, Patrick
%A Ungar, Lyle
%A Eichstaedt, Johannes
%Y Specia, Lucia
%Y Post, Matt
%Y Paul, Michael
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F schwartz-etal-2017-dlatk
%X We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.
%R 10.18653/v1/D17-2010
%U https://aclanthology.org/D17-2010
%U https://doi.org/10.18653/v1/D17-2010
%P 55-60
Markdown (Informal)
[DLATK: Differential Language Analysis ToolKit](https://aclanthology.org/D17-2010) (Schwartz et al., EMNLP 2017)
ACL
- H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, and Johannes Eichstaedt. 2017. DLATK: Differential Language Analysis ToolKit. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 55–60, Copenhagen, Denmark. Association for Computational Linguistics.