Roman Klinger

University of Stuttgart
0000-0002-2014-6619
http://www.romanklinger.de
Publications109
h-index23
Citations1,707
Highly Influential Citations164
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  • Publications
  • Influence
Overview of BioCreative II gene mention recognition
Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name… Expand
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An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews
TLDR
In this paper, we are concerned with approaches for the automatic detection of irony in texts, which is an important task in a variety of applications including the automatic interpretation of text-based chats, computer interaction or sentiment analysis and opinion mining. Expand
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Detection of IUPAC and IUPAC-like chemical names
TLDR
We present a 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. Expand
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Classical Probabilistic Models and Conditional Random Fields
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UvA-DARE ( Digital Academic Repository ) Overview of BioCreative II gene mention recognition
Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name… Expand
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An Analysis of Annotated Corpora for Emotion Classification in Text
TLDR
We aggregate emotion corpora and aggregate them in a common file format with a common annotation schema and perform cross-corpus classification experiments. Expand
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IEST: WASSA-2018 Implicit Emotions Shared Task
TLDR
We propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Expand
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IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
TLDR
Our 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. Expand
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An Empirical, Quantitative Analysis of the Differences Between Sarcasm and Irony
TLDR
We develop a data-driven model to distinguish sarcasm and irony based on authors' labels of their own Tweets. Expand
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Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
TLDR
We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. Expand
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