| 1 |
Psychological Well-Being of Left-Behind Children in China: Text Mining of the Social Media Website Zhihu.
|
|
|
|
In: International journal of environmental research and public health, vol 19, iss 4 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Mining an English-Chinese parallel Dataset of Financial News
|
|
|
|
In: Journal of Open Humanities Data; Vol 8 (2022); 9 ; 2059-481X (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
“Thou Shalt Not Take the Lord’s Name in Vain”: A Methodological Proposal to Identify Religious Hate Content on Digital Social Networks
|
|
|
|
In: International Journal of Communication; Vol 16 (2022); 22 ; 1932-8036 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
WS16: Italian heritage: Using corpus data to map phonological patterns in Brazilian Veneto ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining
|
|
|
|
In: Bioengineering; Volume 9; Issue 3; Pages: 124 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
|
|
|
|
In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Text Mining from Free Unstructured Text: An Experiment of Time Series Retrieval for Volcano Monitoring
|
|
|
|
In: Applied Sciences; Volume 12; Issue 7; Pages: 3503 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
A Novel Approach for Semantic Extractive Text Summarization
|
|
|
|
In: Applied Sciences; Volume 12; Issue 9; Pages: 4479 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate
|
|
|
|
In: Applied Sciences; Volume 12; Issue 6; Pages: 2952 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information
|
|
|
|
In: Applied Sciences; Volume 12; Issue 3; Pages: 1572 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
|
|
|
|
In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Psychological Well-Being of Left-Behind Children in China: Text Mining of the Social Media Website Zhihu
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 4; Pages: 2127 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
|
|
|
|
In: Information; Volume 13; Issue 3; Pages: 152 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Preparing Legal Documents for NLP Analysis: Improving the Classification of Text Elements by Using Page Features
|
|
|
|
Abstract:
Legal documents often have a complex layout with many different headings, headers and footers, side notes, etc. For the further processing, it is important to extract these individual components correctly from a legally binding document, for example a signed PDF. A common approach to do so is to classify each (text) region of a page using its geometric and textual features. This approach works well, when the training and test data have a similar structure and when the documents of a collection to be analyzed have a rather uniform layout. We show that the use of global page properties can improve the accuracy of text element classification: we first classify each page into one of three layout types. After that, we can train a classifier for each of the three page types and thereby improve the accuracy on a manually annotated collection of 70 legal documents consisting of 20,938 text elements. When we split by page type, we achieve an improvement from 0.95 to 0.98 for single-column pages with left marginalia and from 0.95 to 0.96 for double-column pages. We developed our own feature-based method for page layout detection, which we benchmark against a standard implementation of a CNN image classifier. The approach presented here is based on corpus of freely available German contracts and general terms and conditions. Both the corpus and all manual annotations are made freely available. The method is language agnostic.
|
|
Keyword:
Automatische Klassifikation; Bilderkennung; ddc:020; Dokumentanalyse; Maschinelles Lernen; Rechtswissenschaften; Sachtext; Text Mining
|
|
URL: https://serwiss.bib.hs-hannover.de/files/2161/csit120102.pdf
https://serwiss.bib.hs-hannover.de/frontdoor/index/index/docId/2161
http://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-21618
https://doi.org/10.25968/opus-2161
https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-21618
|
|
BASE
|
|
Hide details
|
|
|
| 17 |
Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
[R] Source Code der Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona-Source) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
[R] Source Code der Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona-Source) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
