Search Results for author:
Found 31 papers, 16 papers with code
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations
Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user.
Factuality Detection using Machine Translation -- a Use Case for German Clinical Text
Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed.
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017).
Multilingual Relation Classification via Efficient and Effective Prompting
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes.
Full-Text Argumentation Mining on Scientific Publications
In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks.
Confidence estimation of classification based on the distribution of the neural network output layer
In this paper, we propose novel methods that, given a neural network classification model, estimate uncertainty of particular predictions generated by this model.
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying important input features.
Why only Micro-F1? Class Weighting of Measures for Relation Classification
Relation classification models are conventionally evaluated using only a single measure, e. g., micro-F1, macro-F1 or AUC.
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data.
Contrastive Learning
Low Resource Named Entity Recognition
+4
MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain
We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities.
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Definition Extraction systems are a valuable knowledge source for both humans and algorithms.
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets.
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE).
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models.
SIA: A Scalable Interoperable Annotation Server for Biomedical Named Entities
Recent years showed a strong increase in biomedical sciences and an inherent increase in publication volume.
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research.
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge.
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset.
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain.
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels.
Improving Relation Extraction by Pre-trained Language Representations
Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions.
Ranked #24 on
Relation Extraction
on SemEval-2010 Task-8
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs
Comments on web news contain controversies that manifest as inter-group agreement-conflicts.
Streaming Text Analytics for Real-Time Event Recognition
We also present promising experimental results for the event extraction component of our system, which recognizes a novel set of event types.
Common Round: Application of Language Technologies to Large-Scale Web Debates
Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking.
TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns
Some express a relation that entails the target relation.
Relation- and Phrase-level Linking of FrameNet with Sar-graphs
Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data.
GerNED: A German Corpus for Named Entity Disambiguation
Determining the real-world referents for name mentions of persons, organizations and other named entities in texts has become an important task in many information retrieval scenarios and is referred to as Named Entity Disambiguation (NED).
