Roman Klinger

University of Stuttgart
0000-0002-2014-6619
https://www.romanklinger.de
Publications132
h-index25
Citations2,159
Highly Influential Citations206
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  • Publications
  • Influence
Overview of BioCreative II gene mention recognition
TLDR
It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.
An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews
TLDR
This paper forms the problem as a supervised classification task and evaluates different classifiers, reaching an F1-measure of up to 74 % using logistic regression.
Classical Probabilistic Models and Conditional Random Fields
Detection of IUPAC and IUPAC-like chemical names
TLDR
This work presents a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IupAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools.
An Analysis of Annotated Corpora for Emotion Classification in Text
TLDR
A survey of the datasets is carried out, and a subset of corpora is better classified with models trained on a different corpus, which simplifies the choice of the most appropriate resources for developing a model for a novel domain.
IEST: WASSA-2018 Implicit Emotions Shared Task
TLDR
A shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions, and is called the Implicit Emotion Shared Task (IEST) because the systems has to infer the emotion mostly from the context.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
TLDR
This paper compares several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class) and shows that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-BSTMs are particularly good at fine-grained sentiment tasks.
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
TLDR
The SemEval 2016 stance and sentiment dataset is extended with emotion annotation to investigate annotation reliability and annotation merging, and the relation between emotion annotation and the other annotation layers (stance, sentiment).
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
TLDR
This submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms, which reveals that performance is increased by providing cross-emotional intensity predictions.
SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German
TLDR
The Sentiment Corpus of App Reviews (SCARE), which contains fine-grained annotations of application aspects, subjective (evaluative) phrases and relations between both is created and is available to the research community to support the development of sentiment analysis methods on mobile application reviews.
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