---
_id: '27507'
abstract:
- lang: eng
text: Accurate real estate appraisal is essential in decision making processes of
financial institutions, governments, and trending real estate platforms like Zillow.
One of the most important factors of a property’s value is its location. However,
creating accurate quantifications of location remains a challenge. While traditional
approaches rely on Geographical Information Systems (GIS), recently unstructured
data in form of images was incorporated in the appraisal process, but text data
remains an untapped reservoir. Our study shows that using text data in form of
geolocated Wikipedia articles can increase predictive performance over traditional
GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to
automatically extract geographically weighted vector representations for text
is established and used alongside traditional structural housing features to make
predictions and to uncover local patterns on sale price for real estate transactions
between 2015 and 2020 in Allegheny County, Pennsylvania.
author:
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
in Real Estate Appraisal. In: 55th Annual Hawaii International Conference on
System Sciences (HICSS-55). ; 2022.'
apa: Heuwinkel, T., Kucklick, J.-P., & Müller, O. (2022). Using Geolocated Text
to Quantify Location in Real Estate Appraisal. 55th Annual Hawaii International
Conference on System Sciences (HICSS-55). Hawaii International Conference
on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
Text to Quantify Location in Real Estate Appraisal.” In 55th Annual Hawaii
International Conference on System Sciences (HICSS-55), 2022.
ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
Location in Real Estate Appraisal,” presented at the Hawaii International Conference
on System Science (HICSS), Virtual, 2022.
mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
Estate Appraisal.” 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
Conference on System Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '31054'
abstract:
- lang: eng
text: This paper aims at discussing past limitations set in sentiment analysis research
regarding explicit and implicit mentions of opinions. Previous studies have regularly
neglected this question in favor of methodical research on standard-datasets.
Furthermore, they were limited to linguistically less-diverse domains, such as
commercial product reviews. We face this issue by annotating a German-language
physician review dataset that contains numerous implicit, long, and complex statements
that indicate aspect ratings, such as the physician’s friendliness. We discuss
the nature of implicit statements and present various samples to illustrate the
challenge described.
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
id: '38837'
last_name: Bäumer
citation:
ama: 'Kersting J, Bäumer FS. Implicit Statements in Healthcare Reviews: A Challenge
for Sentiment Analysis. In: Kersting J, ed. Proceedings of the Fourteenth International
Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track
AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications.
IARIA; 2022:5-9.'
apa: 'Kersting, J., & Bäumer, F. S. (2022). Implicit Statements in Healthcare
Reviews: A Challenge for Sentiment Analysis. In J. Kersting (Ed.), Proceedings
of the Fourteenth International Conference on Pervasive Patterns and Applications
(PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data
Science for Real-World Applications (pp. 5–9). IARIA.'
bibtex: '@inproceedings{Kersting_Bäumer_2022, place={Barcelona, Spain}, title={Implicit
Statements in Healthcare Reviews: A Challenge for Sentiment Analysis}, booktitle={Proceedings
of the Fourteenth International Conference on Pervasive Patterns and Applications
(PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data
Science for Real-World Applications}, publisher={IARIA}, author={Kersting, Joschka
and Bäumer, Frederik Simon}, editor={Kersting, Joschka}, year={2022}, pages={5–9}
}'
chicago: 'Kersting, Joschka, and Frederik Simon Bäumer. “Implicit Statements in
Healthcare Reviews: A Challenge for Sentiment Analysis.” In Proceedings of
the Fourteenth International Conference on Pervasive Patterns and Applications
(PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data
Science for Real-World Applications, edited by Joschka Kersting, 5–9. Barcelona,
Spain: IARIA, 2022.'
ieee: 'J. Kersting and F. S. Bäumer, “Implicit Statements in Healthcare Reviews:
A Challenge for Sentiment Analysis,” in Proceedings of the Fourteenth International
Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track
AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications,
Barcelona, Spain, 2022, pp. 5–9.'
mla: 'Kersting, Joschka, and Frederik Simon Bäumer. “Implicit Statements in Healthcare
Reviews: A Challenge for Sentiment Analysis.” Proceedings of the Fourteenth
International Conference on Pervasive Patterns and Applications (PATTERNS 2022):
Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World
Applications, edited by Joschka Kersting, IARIA, 2022, pp. 5–9.'
short: 'J. Kersting, F.S. Bäumer, in: J. Kersting (Ed.), Proceedings of the Fourteenth
International Conference on Pervasive Patterns and Applications (PATTERNS 2022):
Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World
Applications, IARIA, Barcelona, Spain, 2022, pp. 5–9.'
conference:
location: Barcelona, Spain
name: The Fourteenth International Conference on Pervasive Patterns and Applications
(PATTERNS 2022)
start_date: 2022-03
date_created: 2022-05-04T08:12:09Z
date_updated: 2022-12-01T13:40:11Z
ddc:
- '006'
editor:
- first_name: Joschka
full_name: Kersting, Joschka
last_name: Kersting
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2022-12-01T13:39:48Z
date_updated: 2022-12-01T13:39:48Z
file_id: '34172'
file_name: Kersting & Bäumer (2022), Kersting2022.pdf
file_size: 155548
relation: main_file
success: 1
file_date_updated: 2022-12-01T13:39:48Z
has_accepted_license: '1'
keyword:
- Sentiment analysis
- Natural language processing
- Aspect phrase extraction
language:
- iso: eng
page: 5-9
place: Barcelona, Spain
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
name: 'SFB 901 - B1: SFB 901 - Subproject B1'
publication: 'Proceedings of the Fourteenth International Conference on Pervasive
Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial
Intelligence - Data Science for Real-World Applications'
publication_status: published
publisher: IARIA
status: public
title: 'Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis'
type: conference
user_id: '58701'
year: '2022'
...
---
_id: '26049'
abstract:
- lang: eng
text: 'Content is the new oil. Users consume billions of terabytes a day while surfing
on news sites or blogs, posting on social media sites, and sending chat messages
around the globe. While content is heterogeneous, the dominant form of web content
is text. There are situations where more diversity needs to be introduced into
text content, for example, to reuse it on websites or to allow a chatbot to base
its models on the information conveyed rather than of the language used. In order
to achieve this, paraphrasing techniques have been developed: One example is Text
spinning, a technique that automatically paraphrases text while leaving the intent
intact. This makes it easier to reuse content, or to change the language generated
by the bot more human. One method for modifying texts is a combination of translation
and back-translation. This paper presents NATTS, a naive approach that uses transformer-based
translation models to create diversified text, combining translation steps in
one model. An advantage of this approach is that it can be fine-tuned and handle
technical language.'
author:
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
last_name: Bäumer
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Sergej
full_name: Denisov, Sergej
last_name: Denisov
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Bäumer FS, Kersting J, Denisov S, Geierhos M. IN OTHER WORDS: A NAIVE APPROACH
TO TEXT SPINNING. In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET
2021 AND APPLIED COMPUTING 2021. IADIS; 2021:221--225.'
apa: 'Bäumer, F. S., Kersting, J., Denisov, S., & Geierhos, M. (2021). IN OTHER
WORDS: A NAIVE APPROACH TO TEXT SPINNING. PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, 221--225.'
bibtex: '@inproceedings{Bäumer_Kersting_Denisov_Geierhos_2021, title={IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING}, booktitle={PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}, publisher={IADIS},
author={Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos,
Michaela}, year={2021}, pages={221--225} }'
chicago: 'Bäumer, Frederik Simon, Joschka Kersting, Sergej Denisov, and Michaela
Geierhos. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” In PROCEEDINGS
OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021,
221--225. IADIS, 2021.'
ieee: 'F. S. Bäumer, J. Kersting, S. Denisov, and M. Geierhos, “IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING,” in PROCEEDINGS OF THE INTERNATIONAL CONFERENCES
ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, Lisbon, Portugal, 2021, pp.
221--225.'
mla: 'Bäumer, Frederik Simon, et al. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.”
PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED
COMPUTING 2021, IADIS, 2021, pp. 221--225.'
short: 'F.S. Bäumer, J. Kersting, S. Denisov, M. Geierhos, in: PROCEEDINGS OF THE
INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, IADIS,
2021, pp. 221--225.'
conference:
end_date: 15.10.2021
location: Lisbon, Portugal
name: 18th International Conference on Applied Computing
start_date: 13.10.2021
date_created: 2021-10-11T15:26:58Z
date_updated: 2022-01-06T06:57:16Z
ddc:
- '000'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2021-10-15T15:54:41Z
date_updated: 2021-10-15T15:54:41Z
file_id: '26282'
file_name: Bäumer et al. (2021), Baeumer2021.pdf
file_size: 411667
relation: main_file
success: 1
file_date_updated: 2021-10-15T15:54:41Z
has_accepted_license: '1'
keyword:
- Software Requirements
- Natural Language Processing
- Transfer Learning
- On-The-Fly Computing
language:
- iso: eng
page: 221--225
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND
APPLIED COMPUTING 2021
publisher: IADIS
status: public
title: 'IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING'
type: conference
user_id: '58701'
year: '2021'
...
---
_id: '20212'
abstract:
- lang: eng
text: "Ideational impact refers to the uptake of a paper's ideas and concepts by
subsequent research. It is defined in stark contrast to total citation impact,
a measure predominantly used in research evaluation that assumes that all citations
are equal. Understanding ideational impact is critical for evaluating research
impact and understanding how scientific disciplines build a cumulative tradition.
Research has only recently developed automated citation classification techniques
to distinguish between different types of citations and generally does not emphasize
the conceptual content of the citations and its ideational impact. To address
this problem, we develop Deep Content-enriched Ideational Impact Classification
(Deep-CENIC) as the first automated approach for ideational impact classification
to support researchers' literature search practices. We evaluate Deep-CENIC on
1,256 papers citing 24 information systems review articles from the IT business
value domain. We show that Deep-CENIC significantly outperforms state-of-the-art
benchmark models. We contribute to information systems research by operationalizing
the concept of ideational impact, designing a recommender system for academic
papers based on deep learning techniques, and empirically exploring the ideational
impact of the IT business value domain.\r\n"
article_number: '113432'
author:
- first_name: Julian
full_name: Prester, Julian
last_name: Prester
- first_name: Gerit
full_name: Wagner, Gerit
last_name: Wagner
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Nik Rushdi
full_name: Hassan, Nik Rushdi
last_name: Hassan
citation:
ama: 'Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact
of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.
Decision Support Systems. 2021;140(January).'
apa: 'Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2021). Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach. Decision Support Systems, 140(January),
Article 113432.'
bibtex: '@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach}, volume={140}, number={January113432}, journal={Decision Support Systems},
author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi},
year={2021} }'
chicago: 'Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach.” Decision Support Systems 140, no. January (2021).'
ieee: 'J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach,” Decision Support Systems, vol. 140, no. January, Art. no. 113432,
2021.'
mla: 'Prester, Julian, et al. “Classifying the Ideational Impact of Information
Systems Review Articles: A Content-Enriched Deep Learning Approach.” Decision
Support Systems, vol. 140, no. January, 113432, 2021.'
short: J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems
140 (2021).
date_created: 2020-10-27T13:28:21Z
date_updated: 2022-06-10T06:55:32Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2020-10-27T13:31:01Z
date_updated: 2020-10-27T13:31:01Z
file_id: '20213'
file_name: DECSUP-D-20-00312 - PREPUBLICATION.pdf
file_size: 440903
relation: main_file
file_date_updated: 2020-10-27T13:31:01Z
has_accepted_license: '1'
intvolume: ' 140'
issue: January
keyword:
- Ideational impact
- citation classification
- academic recommender systems
- natural language processing
- deep learning
- cumulative tradition
language:
- iso: eng
oa: '1'
publication: Decision Support Systems
status: public
title: 'Classifying the Ideational Impact of Information Systems Review Articles:
A Content-Enriched Deep Learning Approach'
type: journal_article
user_id: '72850'
volume: 140
year: '2021'
...
---
_id: '18686'
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
id: '38837'
last_name: Bäumer
citation:
ama: 'Kersting J, Bäumer FS. SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED
APPROACH. In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING
2020. IADIS; 2020:119--123.'
apa: 'Kersting, J., & Bäumer, F. S. (2020). SEMANTIC TAGGING OF REQUIREMENT
DESCRIPTIONS: A TRANSFORMER-BASED APPROACH. PROCEEDINGS OF THE INTERNATIONAL
CONFERENCE ON APPLIED COMPUTING 2020, 119--123.'
bibtex: '@inproceedings{Kersting_Bäumer_2020, title={SEMANTIC TAGGING OF REQUIREMENT
DESCRIPTIONS: A TRANSFORMER-BASED APPROACH}, booktitle={PROCEEDINGS OF THE INTERNATIONAL
CONFERENCE ON APPLIED COMPUTING 2020}, publisher={IADIS}, author={Kersting, Joschka
and Bäumer, Frederik Simon}, year={2020}, pages={119--123} }'
chicago: 'Kersting, Joschka, and Frederik Simon Bäumer. “SEMANTIC TAGGING OF REQUIREMENT
DESCRIPTIONS: A TRANSFORMER-BASED APPROACH.” In PROCEEDINGS OF THE INTERNATIONAL
CONFERENCE ON APPLIED COMPUTING 2020, 119--123. IADIS, 2020.'
ieee: 'J. Kersting and F. S. Bäumer, “SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS:
A TRANSFORMER-BASED APPROACH,” in PROCEEDINGS OF THE INTERNATIONAL CONFERENCE
ON APPLIED COMPUTING 2020, Lisbon, Portugal, 2020, pp. 119--123.'
mla: 'Kersting, Joschka, and Frederik Simon Bäumer. “SEMANTIC TAGGING OF REQUIREMENT
DESCRIPTIONS: A TRANSFORMER-BASED APPROACH.” PROCEEDINGS OF THE INTERNATIONAL
CONFERENCE ON APPLIED COMPUTING 2020, IADIS, 2020, pp. 119--123.'
short: 'J. Kersting, F.S. Bäumer, in: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE
ON APPLIED COMPUTING 2020, IADIS, 2020, pp. 119--123.'
conference:
end_date: 20.11.2020
location: Lisbon, Portugal
name: 17th International Conference on Applied Computing
start_date: 18.11.2020
date_created: 2020-08-31T10:59:54Z
date_updated: 2022-01-06T06:53:51Z
ddc:
- '000'
department:
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2020-11-19T17:29:03Z
date_updated: 2020-11-19T17:29:03Z
file_id: '20443'
file_name: Kersting & Bäumer (2020), Kersting2020d.pdf
file_size: 1064877
relation: main_file
success: 1
file_date_updated: 2020-11-19T17:29:03Z
has_accepted_license: '1'
keyword:
- Software Requirements
- Natural Language Processing
- Transfer Learning
- On-The-Fly Computing
language:
- iso: eng
page: 119--123
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020
publisher: IADIS
status: public
title: 'SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH'
type: conference
user_id: '58701'
year: '2020'
...
---
_id: '15580'
abstract:
- lang: eng
text: This paper deals with aspect phrase extraction and classification in sentiment
analysis. We summarize current approaches and datasets from the domain of aspect-based
sentiment analysis. This domain detects sentiments expressed for individual aspects
in unstructured text data. So far, mainly commercial user reviews for products
or services such as restaurants were investigated. We here present our dataset
consisting of German physician reviews, a sensitive and linguistically complex
field. Furthermore, we describe the annotation process of a dataset for supervised
learning with neural networks. Moreover, we introduce our model for extracting
and classifying aspect phrases in one step, which obtains an F1-score of 80%.
By applying it to a more complex domain, our approach and results outperform previous
approaches.
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. Aspect Phrase Extraction in Sentiment Analysis with
Deep Learning. In: Proceedings of the 12th International Conference on Agents
and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language
Processing in Artificial Intelligence (NLPinAI 2020). Setúbal, Portugal: SCITEPRESS;
2020:391--400.'
apa: 'Kersting, J., & Geierhos, M. (2020). Aspect Phrase Extraction in Sentiment
Analysis with Deep Learning. In Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020) (pp. 391--400).
Setúbal, Portugal: SCITEPRESS.'
bibtex: '@inproceedings{Kersting_Geierhos_2020, place={Setúbal, Portugal}, title={Aspect
Phrase Extraction in Sentiment Analysis with Deep Learning}, booktitle={Proceedings
of the 12th International Conference on Agents and Artificial Intelligence (ICAART
2020) -- Special Session on Natural Language Processing in Artificial Intelligence
(NLPinAI 2020)}, publisher={SCITEPRESS}, author={Kersting, Joschka and Geierhos,
Michaela}, year={2020}, pages={391--400} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in
Sentiment Analysis with Deep Learning.” In Proceedings of the 12th International
Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session
on Natural Language Processing in Artificial Intelligence (NLPinAI 2020),
391--400. Setúbal, Portugal: SCITEPRESS, 2020.'
ieee: J. Kersting and M. Geierhos, “Aspect Phrase Extraction in Sentiment Analysis
with Deep Learning,” in Proceedings of the 12th International Conference on
Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), Valetta, Malta,
2020, pp. 391--400.
mla: Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in Sentiment
Analysis with Deep Learning.” Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS,
2020, pp. 391--400.
short: 'J. Kersting, M. Geierhos, in: Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS, Setúbal,
Portugal, 2020, pp. 391--400.'
conference:
location: Valetta, Malta
name: International Conference on Agents and Artificial Intelligence (ICAART) -- Special
Session on Natural Language Processing in Artificial Intelligence (NLPinAI)
date_created: 2020-01-15T08:35:07Z
date_updated: 2022-01-06T06:52:29Z
ddc:
- '000'
department:
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2020-09-18T09:27:00Z
date_updated: 2020-09-18T09:27:00Z
file_id: '19576'
file_name: Kersting & Geierhos (2020), Kersting2020.pdf
file_size: 421780
relation: main_file
success: 1
file_date_updated: 2020-09-18T09:27:00Z
has_accepted_license: '1'
keyword:
- Deep Learning
- Natural Language Processing
- Aspect-based Sentiment Analysis
language:
- iso: eng
page: 391--400
place: Setúbal, Portugal
project:
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
- _id: '9'
name: SFB 901 - Subproject B1
publication: Proceedings of the 12th International Conference on Agents and Artificial
Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in
Artificial Intelligence (NLPinAI 2020)
publisher: SCITEPRESS
status: public
title: Aspect Phrase Extraction in Sentiment Analysis with Deep Learning
type: conference
user_id: '58701'
year: '2020'
...
---
_id: '8312'
author:
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
id: '38837'
last_name: Bäumer
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Bäumer FS, Geierhos M. Requirements Engineering in OTF-Computing. In: Encyclopedia.Pub.
Basel, Switzerland: MDPI; 2019.'
apa: 'Bäumer, F. S., & Geierhos, M. (2019). Requirements Engineering in OTF-Computing.
In encyclopedia.pub. Basel, Switzerland: MDPI.'
bibtex: '@inbook{Bäumer_Geierhos_2019, place={Basel, Switzerland}, title={Requirements
Engineering in OTF-Computing}, booktitle={encyclopedia.pub}, publisher={MDPI},
author={Bäumer, Frederik Simon and Geierhos, Michaela}, year={2019} }'
chicago: 'Bäumer, Frederik Simon, and Michaela Geierhos. “Requirements Engineering
in OTF-Computing.” In Encyclopedia.Pub. Basel, Switzerland: MDPI, 2019.'
ieee: 'F. S. Bäumer and M. Geierhos, “Requirements Engineering in OTF-Computing,”
in encyclopedia.pub, Basel, Switzerland: MDPI, 2019.'
mla: Bäumer, Frederik Simon, and Michaela Geierhos. “Requirements Engineering in
OTF-Computing.” Encyclopedia.Pub, MDPI, 2019.
short: 'F.S. Bäumer, M. Geierhos, in: Encyclopedia.Pub, MDPI, Basel, Switzerland,
2019.'
date_created: 2019-03-05T08:54:37Z
date_updated: 2022-01-06T07:03:53Z
department:
- _id: '36'
- _id: '1'
- _id: '579'
keyword:
- OTF Computing
- Natural Language Processing
- Requirements Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://encyclopedia.pub/131
oa: '1'
place: Basel, Switzerland
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: encyclopedia.pub
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: Requirements Engineering in OTF-Computing
type: encyclopedia_article
user_id: '42496'
year: '2019'
...
---
_id: '2331'
abstract:
- lang: eng
text: A user generally writes software requirements in ambiguous and incomplete
form by using natural language; therefore, a software developer may have difficulty
in clearly understanding what the meanings are. To solve this problem with automation,
we propose a classifier for semantic annotation with manually pre-defined semantic
categories. To improve our classifier, we carefully designed syntactic features
extracted by constituency and dependency parsers. Even with a small dataset and
a large number of classes, our proposed classifier records an accuracy of 0.75,
which outperforms the previous model, REaCT.
article_type: original
author:
- first_name: 'Yeongsu '
full_name: 'Kim, Yeongsu '
last_name: Kim
- first_name: Seungwoo
full_name: Lee, Seungwoo
last_name: Lee
- first_name: Markus
full_name: Dollmann, Markus
id: '27578'
last_name: Dollmann
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: Kim Y, Lee S, Dollmann M, Geierhos M. Improving Classifiers for Semantic Annotation
of Software Requirements with Elaborate Syntactic Structure. International
Journal of Advanced Science and Technology. 2018;112:123-136. doi:10.14257/ijast.2018.112.12
apa: Kim, Y., Lee, S., Dollmann, M., & Geierhos, M. (2018). Improving Classifiers
for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.
International Journal of Advanced Science and Technology, 112, 123–136.
https://doi.org/10.14257/ijast.2018.112.12
bibtex: '@article{Kim_Lee_Dollmann_Geierhos_2018, title={Improving Classifiers for
Semantic Annotation of Software Requirements with Elaborate Syntactic Structure},
volume={112}, DOI={10.14257/ijast.2018.112.12},
journal={International Journal of Advanced Science and Technology}, publisher={SERSC
Australia}, author={Kim, Yeongsu and Lee, Seungwoo and Dollmann, Markus and Geierhos,
Michaela}, year={2018}, pages={123–136} }'
chicago: 'Kim, Yeongsu , Seungwoo Lee, Markus Dollmann, and Michaela Geierhos. “Improving
Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic
Structure.” International Journal of Advanced Science and Technology 112
(2018): 123–36. https://doi.org/10.14257/ijast.2018.112.12.'
ieee: Y. Kim, S. Lee, M. Dollmann, and M. Geierhos, “Improving Classifiers for Semantic
Annotation of Software Requirements with Elaborate Syntactic Structure,” International
Journal of Advanced Science and Technology, vol. 112, pp. 123–136, 2018.
mla: Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software
Requirements with Elaborate Syntactic Structure.” International Journal of
Advanced Science and Technology, vol. 112, SERSC Australia, 2018, pp. 123–36,
doi:10.14257/ijast.2018.112.12.
short: Y. Kim, S. Lee, M. Dollmann, M. Geierhos, International Journal of Advanced
Science and Technology 112 (2018) 123–136.
date_created: 2018-04-13T09:19:22Z
date_updated: 2022-01-06T06:55:49Z
ddc:
- '000'
department:
- _id: '36'
- _id: '1'
- _id: '579'
doi: 10.14257/ijast.2018.112.12
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:16:29Z
date_updated: 2018-11-02T15:16:29Z
file_id: '5297'
file_name: 12.pdf
file_size: 586968
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:16:29Z
has_accepted_license: '1'
intvolume: ' 112'
keyword:
- Software Engineering
- Natural Language Processing
- Semantic Annotation
- Machine Learning
- Feature Engineering
- Syntactic Structure
language:
- iso: eng
page: 123-136
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: International Journal of Advanced Science and Technology
publication_identifier:
eissn:
- 2207-6360
issn:
- 2005-4238
publication_status: published
publisher: SERSC Australia
quality_controlled: '1'
status: public
title: Improving Classifiers for Semantic Annotation of Software Requirements with
Elaborate Syntactic Structure
type: journal_article
user_id: '477'
volume: 112
year: '2018'
...
---
_id: '44'
abstract:
- lang: eng
text: Natural language software requirements descriptions enable end users to formulate
their wishes and expectations for a future software product without much prior
knowledge in requirements engineering. However, these descriptions are susceptible
to linguistic inaccuracies such as ambiguities and incompleteness that can harm
the development process. There is a number of software solutions that can detect
deficits in requirements descriptions and partially solve them, but they are often
hard to use and not suitable for end users. For this reason, we develop a software
system that helps end-users to create unambiguous and complete requirements descriptions
by combining existing expert tools and controlling them using automatic compensation
strategies. In order to recognize the necessity of individual compensation methods
in the descriptions, we have developed linguistic indicators, which we present
in this paper. Based on these indicators, the whole text analysis pipeline is
ad-hoc configured and thus adapted to the individual circumstances of a requirements
description.
author:
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
id: '38837'
last_name: Bäumer
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Bäumer FS, Geierhos M. Flexible Ambiguity Resolution and Incompleteness Detection
in Requirements Descriptions via an Indicator-based Configuration of Text Analysis
Pipelines. In: Proceedings of the 51st Hawaii International Conference on System
Sciences. ; 2018:5746-5755. doi:10125/50609'
apa: Bäumer, F. S., & Geierhos, M. (2018). Flexible Ambiguity Resolution and
Incompleteness Detection in Requirements Descriptions via an Indicator-based Configuration
of Text Analysis Pipelines. In Proceedings of the 51st Hawaii International
Conference on System Sciences (pp. 5746–5755). Big Island, Waikoloa Village.
https://doi.org/10125/50609
bibtex: '@inproceedings{Bäumer_Geierhos_2018, title={Flexible Ambiguity Resolution
and Incompleteness Detection in Requirements Descriptions via an Indicator-based
Configuration of Text Analysis Pipelines}, DOI={10125/50609},
booktitle={Proceedings of the 51st Hawaii International Conference on System Sciences},
author={Bäumer, Frederik Simon and Geierhos, Michaela}, year={2018}, pages={5746–5755}
}'
chicago: Bäumer, Frederik Simon, and Michaela Geierhos. “Flexible Ambiguity Resolution
and Incompleteness Detection in Requirements Descriptions via an Indicator-Based
Configuration of Text Analysis Pipelines.” In Proceedings of the 51st Hawaii
International Conference on System Sciences, 5746–55, 2018. https://doi.org/10125/50609.
ieee: F. S. Bäumer and M. Geierhos, “Flexible Ambiguity Resolution and Incompleteness
Detection in Requirements Descriptions via an Indicator-based Configuration of
Text Analysis Pipelines,” in Proceedings of the 51st Hawaii International Conference
on System Sciences, Big Island, Waikoloa Village, 2018, pp. 5746–5755.
mla: Bäumer, Frederik Simon, and Michaela Geierhos. “Flexible Ambiguity Resolution
and Incompleteness Detection in Requirements Descriptions via an Indicator-Based
Configuration of Text Analysis Pipelines.” Proceedings of the 51st Hawaii International
Conference on System Sciences, 2018, pp. 5746–55, doi:10125/50609.
short: 'F.S. Bäumer, M. Geierhos, in: Proceedings of the 51st Hawaii International
Conference on System Sciences, 2018, pp. 5746–5755.'
conference:
end_date: 2018-01-06
location: Big Island, Waikoloa Village
name: 51st Hawaii International Conference on System Sciences (HICSS 2018)
start_date: 2018-01-03
date_created: 2017-10-17T12:40:59Z
date_updated: 2022-01-06T07:01:01Z
ddc:
- '000'
department:
- _id: '36'
- _id: '1'
- _id: '579'
doi: 10125/50609
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:32:35Z
date_updated: 2018-11-02T14:32:35Z
file_id: '5274'
file_name: paper0722.pdf
file_size: 753693
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:32:35Z
has_accepted_license: '1'
keyword:
- 'Software Product Lines: Engineering'
- Services
- and Management
- Ambiguities
- Incompleteness
- Natural Language Processing
- Software Requirements
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/50609/1/paper0722.pdf
oa: '1'
page: 5746-5755
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: Proceedings of the 51st Hawaii International Conference on System Sciences
publication_identifier:
isbn:
- 978-0-9981331-1-9
publication_status: published
quality_controlled: '1'
status: public
title: Flexible Ambiguity Resolution and Incompleteness Detection in Requirements
Descriptions via an Indicator-based Configuration of Text Analysis Pipelines
type: conference
user_id: '477'
year: '2018'
...
---
_id: '1098'
abstract:
- lang: eng
text: An end user generally writes down software requirements in ambiguous expressions
using natural language; hence, a software developer attuned to programming language
finds it difficult to understand th meaning of the requirements. To solve this
problem we define semantic categories for disambiguation and classify/annotate
the requirement into the categories by using machine-learning models. We extensively
use a language frame closely related to such categories for designing features
to overcome the problem of insufficient training data compare to the large number
of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming
the previous model, REaCT.
article_type: original
author:
- first_name: Yeong-Su
full_name: Kim, Yeong-Su
last_name: Kim
- first_name: 'Seung-Woo '
full_name: 'Lee, Seung-Woo '
last_name: Lee
- first_name: Markus
full_name: Dollmann, Markus
id: '27578'
last_name: Dollmann
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: Kim Y-S, Lee S-W, Dollmann M, Geierhos M. Semantic Annotation of Software Requirements
with Language Frame. International Journal of Software Engineering for Smart
Device. 2017;4(2):1-6.
apa: Kim, Y.-S., Lee, S.-W., Dollmann, M., & Geierhos, M. (2017). Semantic Annotation
of Software Requirements with Language Frame. International Journal of Software
Engineering for Smart Device, 4(2), 1–6.
bibtex: '@article{Kim_Lee_Dollmann_Geierhos_2017, title={Semantic Annotation of
Software Requirements with Language Frame}, volume={4}, number={2}, journal={International
Journal of Software Engineering for Smart Device}, publisher={Global Vision School
Publication}, author={Kim, Yeong-Su and Lee, Seung-Woo and Dollmann, Markus and
Geierhos, Michaela}, year={2017}, pages={1–6} }'
chicago: 'Kim, Yeong-Su, Seung-Woo Lee, Markus Dollmann, and Michaela Geierhos.
“Semantic Annotation of Software Requirements with Language Frame.” International
Journal of Software Engineering for Smart Device 4, no. 2 (2017): 1–6.'
ieee: Y.-S. Kim, S.-W. Lee, M. Dollmann, and M. Geierhos, “Semantic Annotation of
Software Requirements with Language Frame,” International Journal of Software
Engineering for Smart Device, vol. 4, no. 2, pp. 1–6, 2017.
mla: Kim, Yeong-Su, et al. “Semantic Annotation of Software Requirements with Language
Frame.” International Journal of Software Engineering for Smart Device,
vol. 4, no. 2, Global Vision School Publication, 2017, pp. 1–6.
short: Y.-S. Kim, S.-W. Lee, M. Dollmann, M. Geierhos, International Journal of
Software Engineering for Smart Device 4 (2017) 1–6.
date_created: 2018-01-25T15:23:15Z
date_updated: 2022-01-06T06:50:55Z
ddc:
- '000'
department:
- _id: '36'
- _id: '1'
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-12-12T15:30:59Z
date_updated: 2018-12-12T15:30:59Z
file_id: '6196'
file_name: Semantic_Annotation_of_Software_Requirements.pdf
file_size: 244655
relation: main_file
success: 1
file_date_updated: 2018-12-12T15:30:59Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '2'
keyword:
- Natural Language Processing
- Semantic Annotation
- Machine Learning
language:
- iso: eng
page: 1-6
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: International Journal of Software Engineering for Smart Device
publication_identifier:
issn:
- 2205-8494
publication_status: published
publisher: Global Vision School Publication
quality_controlled: '1'
status: public
title: Semantic Annotation of Software Requirements with Language Frame
type: journal_article
user_id: '477'
volume: 4
year: '2017'
...
---
_id: '1118'
abstract:
- lang: ger
text: "Das wesentliche Ziel der vorliegenden Publikation ist die Erstellung von
sprachspezifischen Modulen im Bereich der Biographischen InformationsExtraktion
(BiographIE). Unter Informationsextraktion verstehen wir die automatisierte Analyse
von Dokumenten im Hinblick auf das Entdecken und Normalisieren von semantisch
interessanten Entitäten und deren Eigenschaften.\r\nDas Hauptgewicht der Arbeit
liegt auf sehr detaillierten und umfangreichen linguistischen Grammatiken im Bereich
der Beschreibung von Personen und deren Beziehungen zu anderen relevanten Entitäten
(z.B. Organisationen, Orte, Datums- und Zeitangaben) in Texten. Neben den öffentlichen
und privaten Eigenschaften von Personen (Geburtsdatum, Nationalität etc.) sollen
vor allem alle biographisch relevanten Attribute aus Texten extrahiert werden
können. Dazu gehören in erster Linie berufliche Werdegänge, Anstellungsverhältnisse,
Rollen in Firmen und ähnliche Eigenschaften. Da alle diese Attribute in unzählbar
verschiedenen Formen ausgedrückt werden können, müssen sehr umfangreiche Lexika
und sehr detaillierte grammatische Beschreibungen erstellt werden. Dies geschieht
hauptsächlich bei der systematischen Evaluierung von Korpora. Je umfangreicher
diese sind, desto adäquater werden die erstellten Grammatiken sein. Im Gegensatz
zu den heute üblichen statistischen, auf maschinellem Lernen basierenden Verfahren
setzen wir auch umfangreiche semi-automatisch erstellte, linguistische Module
ein, die dann durch systematische Evaluierung auf Korpora schnell ergänzt und
verbessert werden können.\r\nBasierend auf unseren Extraktionsmethoden ist es
nun möglich, im Bereich der semantischen Suche deutliche Fortschritte zu machen.
Insbesondere Personensuchmaschinen können sich unsere detaillierten Analysemethoden
zu Nutze machen, um beispielsweise zu ermitteln, wer in welcher Funktion bei welcher
Firma von wann bis wann beschäftigt war."
author:
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Geierhos M. BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen. Vol 5. 1st ed. München: Lincom; 2010.'
apa: 'Geierhos, M. (2010). BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen (1st ed., Vol. 5). München: Lincom.'
bibtex: '@book{Geierhos_2010, place={München}, edition={1}, series={Linguistic Resources
for Natural Language Processing}, title={BiographIE - Klassifikation und Extraktion
karrierespezifischer Informationen}, volume={5}, publisher={Lincom}, author={Geierhos,
Michaela}, year={2010}, collection={Linguistic Resources for Natural Language
Processing} }'
chicago: 'Geierhos, Michaela. BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen. 1st ed. Vol. 5. Linguistic Resources for Natural Language Processing.
München: Lincom, 2010.'
ieee: 'M. Geierhos, BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen, 1st ed., vol. 5. München: Lincom, 2010.'
mla: Geierhos, Michaela. BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen. 1st ed., vol. 5, Lincom, 2010.
short: M. Geierhos, BiographIE - Klassifikation und Extraktion karrierespezifischer
Informationen, 1st ed., Lincom, München, 2010.
date_created: 2018-01-29T14:52:46Z
date_updated: 2022-01-06T06:50:57Z
department:
- _id: '36'
- _id: '1'
- _id: '579'
edition: '1'
extern: '1'
intvolume: ' 5'
keyword:
- Natural Language Processing
language:
- iso: ger
page: '286'
place: München
publication_identifier:
isbn:
- '9783862880133'
publication_status: published
publisher: Lincom
series_title: Linguistic Resources for Natural Language Processing
status: public
title: BiographIE - Klassifikation und Extraktion karrierespezifischer Informationen
type: book
user_id: '42496'
volume: 5
year: '2010'
...