Search Results for author:
Found 27 papers, 4 papers with code
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues
The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively.
Investigating Cross-Document Event Coreference for Dutch
In this paper we present baseline results for Event Coreference Resolution (ECR) in Dutch using gold-standard (i. e non-predicted) event mentions.
Variation in the Expression and Annotation of Emotions: A Wizard of Oz Pilot Study
Finally, for the third premise, we observed a positive correlation between the internal-external agreement on emotion labels and the personality traits conscientiousness and extraversion.
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts
In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA).
Aspect-Based Sentiment Analysis (ABSA)
coreference-resolution
+3
D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction
This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora.
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection
This paper describes the approach developed by the LT3 team in the Intended Sarcasm Detection task at SemEval-2022 Task 6.
Irony Detection for Dutch: a Venture into the Implicit
This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach.
SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis
In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations.
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch
A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types.
Extracting Fine-Grained Economic Events from Business News
Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction.
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection
The models are tested on 1, 000 Dutch tweets and 1, 000 captions from TV-shows which have been manually annotated with emotion categories and dimensions.
Exploring Implicit Sentiment Evoked by Fine-grained News Events
We investigate the feasibility of defining sentiment evoked by fine-grained news events.
It’s absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?
However, this is only the case when relying on gold-standard relations and the result is more outspoken for English than for Dutch.
Aspect-Based Sentiment Analysis (ABSA)
coreference-resolution
+2
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition.
Nearest neighbour approaches for Emotion Detection in Tweets
Emotion detection is an important task that can be applied to social media data to discover new knowledge.
LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines
This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines.
TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset
The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER).
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus
Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice.
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content
One of the most persistent characteristics of written user-generated content (UGC) is the use of non-standard words.
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat
Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues.
Comparing MT Approaches for Text Normalization
Regarding NMT, we find that the translations - or normalizations - coming out of this model are far from perfect and that for a low-resource language like Dutch adding additional training data works better than artificially augmenting the data.
Evaluating automatic cross-domain Dutch semantic role annotation
In this paper we present the first corpus where one million Dutch words from a variety of text genres have been annotated with semantic roles.
From keystrokes to annotated process data: Enriching the output of Inputlog with linguistic information
In this paper we report on an extension to the keystroke logging program Inputlog in which we aggregate the logged process data from the keystroke (character) level to the word level.
