Semantic Network Analysis Pipeline-Interactive Text Mining Framework for Exploration of Semantic Flows in Large Corpus of Text

被引:1
|
作者
Cenek, Martin [1 ,4 ]
Bulkow, Rowan [2 ]
Pak, Eric [3 ]
Oyster, Levi [3 ]
Ching, Boyd [3 ]
Mulagada, Ashika [1 ]
机构
[1] Univ Portland, Comp Sci, Portland, OR 90203 USA
[2] Resource Data Inc, Anchorage, AK 99503 USA
[3] Univ Alaska Anchorage, Comp Sci, Anchorage, AK 99508 USA
[4] 5000 N Willamette Blvd, Portland, OR 97203 USA
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 24期
关键词
semantic concept; text mining; computational linguistics; language processing; natural language processing; interactive visualization; MODEL;
D O I
10.3390/app9245302
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Historical topic modeling and semantic concepts exploration in a large corpus of unstructured text remains a hard, opened problem. Despite advancements in natural languages processing tools, statistical linguistics models, graph theory and visualization, there is no framework that combines these piece-wise tools under one roof. We designed and constructed a Semantic Network Analysis Pipeline (SNAP) that is available as an open-source web-service that implements work-flow needed by a data scientist to explore historical semantic concepts in a text corpus. We define a graph theoretic notion of a semantic concept as a flow of closely related tokens through the corpus of text. The modular work-flow pipeline processes text using natural language processing tools, statistical content narrowing, creates semantic networks from lexical token chaining, performs social network analysis of token networks and creates a 3D visualization of the semantic concept flows through corpus for interactive concept exploration. Finally, we illustrate the framework's utility to extract the information from a text corpus of Herman Melville's novel Moby Dick, the transcript of the 2015-2016 United States (U.S.) Senate Hearings on Environment and Public Works, and the Australian Broadcast Corporation's short news articles on rural and science topics.
引用
收藏
页数:15
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